{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NER_aspect_airline_ATIS.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"NYQRU3pRO146"},"source":["![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n","\n","[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/tutorial_docs/examples/colab/component_examples/named_entity_recognition_NER/NER_aspect_airline_ATIS.ipynb)\n","\n","\n","Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example:\n","<br>\n","<br>\n","\n","#Content\n","ATIS dataset provides large number of messages and their associated intents that can be used in training a classifier. Within a chatbot, intent refers to the goal the customer has in mind when typing in a question or comment. While entity refers to the modifier the customer uses to describe their issue, the intent is what they really mean. For example, a user says, ‘I need new shoes.’ The intent behind the message is to browse the footwear on offer. Understanding the intent of the customer is key to implementing a successful chatbot experience for end-user.\n","https://www.kaggle.com/hassanamin/atis-airlinetravelinformationsystem\n","<br>\n","<br>\n","\n","|Tags predicted by this model | \t\n","|------|\n"," | O|\n"," | I-depart_time.end_time|\n"," | B-arrive_date.date_relative|\n"," | I-fromloc.state_name|\n"," | B-depart_date.date_relative|\n"," | B-fromloc.state_code|\n"," | B-meal_description|\n"," | B-depart_time.time_relative|\n"," | I-fare_amount|\n"," | I-fromloc.city_name|\n"," | B-booking_class|\n"," | I-arrive_time.end_time|\n"," | B-return_date.today_relative|\n"," | B-fromloc.state_name|\n"," | B-round_trip|\n"," | B-depart_date.today_relative|\n"," | I-return_date.day_number|\n"," | I-depart_time.start_time|\n"," | B-period_of_day|\n"," | B-arrive_date.day_number|\n"," | B-flight_stop|\n"," | B-depart_date.day_name|\n"," | I-stoploc.city_name|\n"," | I-return_date.today_relative|\n"," | B-class_type|\n"," | B-stoploc.state_code|\n"," | B-economy|\n"," | B-depart_time.end_time|\n"," | B-return_date.date_relative|\n"," | I-fromloc.airport_name|\n"," | B-arrive_date.month_name|\n"," | I-flight_mod|\n"," | B-toloc.airport_code|\n"," | I-depart_time.end_time|\n"," | B-airline_code|\n"," | B-flight_mod|\n"," | B-cost_relative|\n"," | B-state_name|\n"," | B-fromloc.city_name|\n"," | B-depart_time.period_of_day|\n"," | I-city_name|\n"," | B-depart_time.period_mod|\n"," | B-city_name|\n"," | B-meal|\n"," | B-return_date.day_number|\n"," | I-airline_name|\n"," | I-restriction_code|\n"," | B-airline_name|\n"," | B-restriction_code|\n"," | B-flight|\n"," | B-transport_type|\n"," | B-time_relative|\n"," | B-arrive_time.time_relative|\n"," | B-fromloc.airport_code|\n"," | B-time|\n"," | I-toloc.city_name|\n"," | B-toloc.state_name|\n"," | B-meal_code|\n"," | I-arrive_date.day_number|\n"," | B-depart_time.start_time|\n"," | B-month_name|\n"," | B-fromloc.airport_name|\n"," | B-flight_number|\n"," | B-days_code|\n"," | I-meal_description|\n"," | B-fare_basis_code|\n"," | I-cost_relative|\n"," | I-time|\n"," | B-return_time.period_of_day|\n"," | I-depart_time.time|\n"," | B-depart_date.day_number|\n"," | I-economy|\n"," | B-arrive_time.start_time|\n"," | B-return_date.day_name|\n"," | B-return_time.period_mod|\n"," | B-airport_code|\n"," | B-stoploc.airport_code|\n"," | B-flight_time|\n"," | I-transport_type|\n"," | B-depart_date.month_name|\n"," | I-toloc.airport_name|\n"," | B-today_relative|\n"," | I-arrive_time.period_of_day|\n"," | B-day_name|\n"," | B-toloc.city_name|\n"," | B-connect|\n"," | I-round_trip|\n"," | B-depart_time.time|\n"," | B-airport_name|\n"," | B-arrive_time.period_of_day|\n"," | B-stoploc.airport_name|\n"," | I-class_type|\n"," | B-aircraft_code|\n"," | I-return_date.date_relative|\n"," | B-toloc.country_name|\n"," | I-flight_number|\n"," | B-state_code|\n"," | B-or|\n"," | I-depart_date.today_relative|\n"," | B-toloc.airport_name|\n"," | I-arrive_time.time|\n"," | I-flight_time|\n"," | I-state_name|\n"," | I-airport_name|\n"," | I-depart_time.period_of_day|\n"," | B-arrive_time.time|\n"," | B-depart_date.year|\n"," | I-flight_stop|\n"," | I-toloc.state_name|\n"," | B-arrive_date.day_name|\n"," | B-compartment|\n"," | I-depart_date.day_number|\n"," | I-meal_code|\n"," | B-arrive_time.end_time|\n"," | I-today_relative|\n"," | I-arrive_time.start_time|\n"," | B-toloc.state_code|\n"," | B-day_number|\n"," | I-arrive_time.time_relative|\n"," | I-fare_basis_code|\n"," | I-depart_time.time_relative|\n"," | B-return_date.month_name|\n"," | B-stoploc.city_name|\n"," | B-arrive_time.period_mod|\n"," | B-fare_amount|\n"," | B-mod|\n"," | B-arrive_date.today_relative|\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"M2-GiYL6xurJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1649992909768,"user_tz":-300,"elapsed":99299,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"02c425fe-3c67-40ae-cd8a-f1aab3203e44"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","  \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-04-15 03:20:09--  https://setup.johnsnowlabs.com/nlu/colab.sh\n","Resolving setup.johnsnowlabs.com (setup.johnsnowlabs.com)... 51.158.130.125\n","Connecting to setup.johnsnowlabs.com (setup.johnsnowlabs.com)|51.158.130.125|:443... connected.\n","HTTP request sent, awaiting response... 302 Moved Temporarily\n","Location: https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh [following]\n","--2022-04-15 03:20:09--  https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1665 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","-                     0%[                    ]       0  --.-KB/s               Installing  NLU 3.4.3rc2 with  PySpark 3.0.3 and Spark NLP 3.4.2 for Google Colab ...\n","-                   100%[===================>]   1.63K  --.-KB/s    in 0.001s  \n","\n","2022-04-15 03:20:09 (1.64 MB/s) - written to stdout [1665/1665]\n","\n","Get:1 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n","Get:2 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n","Ign:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n","Get:4 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB]\n","Hit:5 http://archive.ubuntu.com/ubuntu bionic InRelease\n","Ign:6 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n","Get:7 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n","Get:8 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release [696 B]\n","Hit:9 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n","Get:10 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release.gpg [836 B]\n","Hit:11 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n","Get:12 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n","Get:13 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [2,695 kB]\n","Get:14 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease [15.9 kB]\n","Get:15 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,490 kB]\n","Hit:16 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n","Get:17 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,947 kB]\n","Get:19 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages [953 kB]\n","Get:20 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [996 kB]\n","Get:21 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [3,134 kB]\n","Get:22 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,268 kB]\n","Get:23 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic/main amd64 Packages [45.3 kB]\n","Fetched 13.8 MB in 3s (4,307 kB/s)\n","Reading package lists... Done\n","tar: spark-3.0.2-bin-hadoop2.7.tgz: Cannot open: No such file or directory\n","tar: Error is not recoverable: exiting now\n","\u001b[K     |████████████████████████████████| 209.1 MB 58 kB/s \n","\u001b[K     |████████████████████████████████| 142 kB 53.6 MB/s \n","\u001b[K     |████████████████████████████████| 505 kB 54.1 MB/s \n","\u001b[K     |████████████████████████████████| 198 kB 59.4 MB/s \n","\u001b[?25h  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Collecting nlu_tmp==3.4.3rc10\n","  Downloading nlu_tmp-3.4.3rc10-py3-none-any.whl (510 kB)\n","\u001b[K     |████████████████████████████████| 510 kB 33.7 MB/s \n","\u001b[?25hRequirement already satisfied: pyarrow>=0.16.0 in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (6.0.1)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (1.21.5)\n","Requirement already satisfied: dataclasses in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (0.6)\n","Requirement already satisfied: spark-nlp<3.5.0,>=3.4.2 in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (3.4.2)\n","Requirement already satisfied: pandas>=1.3.5 in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (1.3.5)\n","Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.3.5->nlu_tmp==3.4.3rc10) (2018.9)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.3.5->nlu_tmp==3.4.3rc10) (2.8.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.3.5->nlu_tmp==3.4.3rc10) (1.15.0)\n","Installing collected packages: nlu-tmp\n","Successfully installed nlu-tmp-3.4.3rc10\n"]}]},{"cell_type":"markdown","metadata":{"id":"Gph8XOL1Pzpl"},"source":["# NLU makes NER easy. \n","\n","You just need to load the NER model via ner.load() and predict on some dataset.    \n","It could be a pandas dataframe with a column named text or just an array of strings."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":718},"id":"pmpZSNvGlyZQ","executionInfo":{"status":"ok","timestamp":1649993134122,"user_tz":-300,"elapsed":224714,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"33626db6-e4be-4299-b73a-7a84f0a37f63"},"source":["import nlu \n","import pandas as pd\n","! wget http://ckl-it.de/wp-content/uploads/2021/01/atis_intents.csv\n","df = pd.read_csv(\"atis_intents.csv\")\n","df.columns = [\"flight\",\"text\"]\n","ner_df = nlu.load('en.ner.aspect.airline',).predict(df[\"text\"],output_level='chunk')\n","ner_df"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-04-15 03:21:47--  http://ckl-it.de/wp-content/uploads/2021/01/atis_intents.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 391936 (383K) [text/csv]\n","Saving to: ‘atis_intents.csv’\n","\n","atis_intents.csv    100%[===================>] 382.75K   843KB/s    in 0.5s    \n","\n","2022-04-15 03:21:48 (843 KB/s) - ‘atis_intents.csv’ saved [391936/391936]\n","\n","nerdl_atis_840b_300d download started this may take some time.\n","Approximate size to download 14.5 MB\n","[OK!]\n","glove_840B_300 download started this may take some time.\n","Approximate size to download 2.3 GB\n","[OK!]\n"]},{"output_type":"execute_result","data":{"text/plain":["                                               document entities_aspect  \\\n","0     what flights are available from pittsburgh to ...      pittsburgh   \n","0     what flights are available from pittsburgh to ...       baltimore   \n","0     what flights are available from pittsburgh to ...        thursday   \n","0     what flights are available from pittsburgh to ...         morning   \n","1     what is the arrival time in san francisco for ...    arrival time   \n","...                                                 ...             ...   \n","4975  does continental fly from boston to san franci...   san francisco   \n","4975  does continental fly from boston to san franci...          denver   \n","4976  is there a delta flight from denver to san fra...           delta   \n","4976  is there a delta flight from denver to san fra...          denver   \n","4976  is there a delta flight from denver to san fra...   san francisco   \n","\n","          entities_aspect_class entities_aspect_confidence  \\\n","0             fromloc.city_name                        1.0   \n","0               toloc.city_name                        1.0   \n","0          depart_date.day_name                        1.0   \n","0     depart_time.period_of_day                     0.9999   \n","1                   flight_time                 0.84714997   \n","...                         ...                        ...   \n","4975            toloc.city_name                     0.9977   \n","4975          stoploc.city_name                        1.0   \n","4976               airline_name                        1.0   \n","4976          fromloc.city_name                        1.0   \n","4976            toloc.city_name                 0.99689996   \n","\n","                                   word_embedding_glove  \n","0     [[-0.038548000156879425, 0.5425199866294861, -...  \n","0     [[-0.038548000156879425, 0.5425199866294861, -...  \n","0     [[-0.038548000156879425, 0.5425199866294861, -...  \n","0     [[-0.038548000156879425, 0.5425199866294861, -...  \n","1     [[-0.038548000156879425, 0.5425199866294861, -...  \n","...                                                 ...  \n","4975  [[-0.13562999665737152, 0.3321700096130371, -0...  \n","4975  [[-0.13562999665737152, 0.3321700096130371, -0...  \n","4976  [[-0.08496099710464478, 0.5019999742507935, 0....  \n","4976  [[-0.08496099710464478, 0.5019999742507935, 0....  \n","4976  [[-0.08496099710464478, 0.5019999742507935, 0....  \n","\n","[16693 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-67d6f485-45d2-4d5c-8922-d229e4cf7193\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>document</th>\n","      <th>entities_aspect</th>\n","      <th>entities_aspect_class</th>\n","      <th>entities_aspect_confidence</th>\n","      <th>word_embedding_glove</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>what flights are available from pittsburgh to ...</td>\n","      <td>pittsburgh</td>\n","      <td>fromloc.city_name</td>\n","      <td>1.0</td>\n","      <td>[[-0.038548000156879425, 0.5425199866294861, -...</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>what flights are available from pittsburgh to ...</td>\n","      <td>baltimore</td>\n","      <td>toloc.city_name</td>\n","      <td>1.0</td>\n","      <td>[[-0.038548000156879425, 0.5425199866294861, -...</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>what flights are available from pittsburgh to ...</td>\n","      <td>thursday</td>\n","      <td>depart_date.day_name</td>\n","      <td>1.0</td>\n","      <td>[[-0.038548000156879425, 0.5425199866294861, -...</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>what flights are available from pittsburgh to ...</td>\n","      <td>morning</td>\n","      <td>depart_time.period_of_day</td>\n","      <td>0.9999</td>\n","      <td>[[-0.038548000156879425, 0.5425199866294861, -...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>what is the arrival time in san francisco for ...</td>\n","      <td>arrival time</td>\n","      <td>flight_time</td>\n","      <td>0.84714997</td>\n","      <td>[[-0.038548000156879425, 0.5425199866294861, -...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>4975</th>\n","      <td>does continental fly from boston to san franci...</td>\n","      <td>san francisco</td>\n","      <td>toloc.city_name</td>\n","      <td>0.9977</td>\n","      <td>[[-0.13562999665737152, 0.3321700096130371, -0...</td>\n","    </tr>\n","    <tr>\n","      <th>4975</th>\n","      <td>does continental fly from boston to san franci...</td>\n","      <td>denver</td>\n","      <td>stoploc.city_name</td>\n","      <td>1.0</td>\n","      <td>[[-0.13562999665737152, 0.3321700096130371, -0...</td>\n","    </tr>\n","    <tr>\n","      <th>4976</th>\n","      <td>is there a delta flight from denver to san fra...</td>\n","      <td>delta</td>\n","      <td>airline_name</td>\n","      <td>1.0</td>\n","      <td>[[-0.08496099710464478, 0.5019999742507935, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>4976</th>\n","      <td>is there a delta flight from denver to san fra...</td>\n","      <td>denver</td>\n","      <td>fromloc.city_name</td>\n","      <td>1.0</td>\n","      <td>[[-0.08496099710464478, 0.5019999742507935, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>4976</th>\n","      <td>is there a delta flight from denver to san fra...</td>\n","      <td>san francisco</td>\n","      <td>toloc.city_name</td>\n","      <td>0.99689996</td>\n","      <td>[[-0.08496099710464478, 0.5019999742507935, 0....</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>16693 rows × 5 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-67d6f485-45d2-4d5c-8922-d229e4cf7193')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-67d6f485-45d2-4d5c-8922-d229e4cf7193 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-67d6f485-45d2-4d5c-8922-d229e4cf7193');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"STc7iOwtljGo"},"source":["## Lets explore our data which the predicted NER tags and visalize them!    \n","\n","We specify [1:] so we dont see the count for the O-tag wich is the most common, since most words in a sentence are not named entities and thus not part of a chunk"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":919},"id":"UDSAYjadlfdK","executionInfo":{"status":"ok","timestamp":1649993262713,"user_tz":-300,"elapsed":1623,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"d1a765c6-a6fa-4995-b04b-8dd1a1ce7440"},"source":["ner_df['entities_aspect'].value_counts()[0:50].plot.bar(title='Occurence of Named Entities in dataset', figsize=(20,14))"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f6b8c8567d0>"]},"metadata":{},"execution_count":6},{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x1008 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAABIoAAAN1CAYAAAAQchC3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdf/BlaV3Y+fcHelFQ5NdMEBigMZCkVmsx1kTJJpu4QYM6GNyUEoxr0JAiu2vMKpg4Ii7GH2RM3KCGDQkrCLguwmIsCYMKohRuImQHBaPirhMYmCH8GPklP8SIPPvHfTp8p9Pd09PdMz0Mr1fVt/rec5577nPv+U7V9Luec3rWWgEAAADAnS72BAAAAAC4fRCKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgDgdmxm/ruZuX5mPjQzf/piz+eWmJkvnpkbbqP3etD+ju58hjEfmpnPucDve8GOOTPXzcyXXIhjAQDnTigCgItgZr5hZv7dzHxkZt45M8+amXte7HndDv1g9XfWWp+51vq1k3fOzNrf452ObPu+mXnebTnJc7Hn/uEdW078/P2zfO1Nospa6237O/qjvf/VM/O3jr5m73/zhfwMt8Yxz8b+7h56R3kfALg9EYoA4DY2M0+ufqD6e9U9qkdUD65eOTN3uY3mcOy2eJ8L4MHVb97MmPtXj7sN5nJrePiOLSd+/tHFnhAA8KlNKAKA29DMfFb1D6pvXmv93FrrD9da11WPrY5X//0ed+eZecrM/PuZ+eDMvH5mHrj3fe7MvHJm3jsz75qZp+ztz5uZ7zvyXje59GmvQvn2mfn16sMzc2xmHjEz/2Zm3j8zb5yZLz4y/tUz870z86/3HF4xM5cc2f/nj7z2+pn5hr3902bmB2fmbXt+/3xm7nqa7+NOM/PUmXnrzLx7Zl4wM/fYx/hQdefqjTPz78/wtf6j6h+cLn7NzP+1V219YGZeMzOfe2Tf82bmn83Mz+4VPf96Zj57Zn5oZt43M7999JK3mbn/zPzUzNw4M2+Zmb97ZN9d9/HeNzO/Vf2ZM8z5jGbmu2fmxfv7+ODM/ObMXL73/Xj1oOpfnViFNDPH9+qXYzPz/dV/Uz1z73/mft1/Wh1zpnM0M5fMzMv2eX3vzPzy0RVbJ83z6DGfNzP/28xcvef8upn542f4jF+/z/t7ZuY7T9r3hTPzK3sO75iZZ56IqDPzmj3sjfvz/bWZudee8437+3/ZzFx25HjfMDNv3vN6y8x83ZF9f3Nm3rRf9/Mz8+DTvc/Znj8A+GQmFAHAbeu/rj69+pdHN661PlS9vPrSvelJ1ddWX1F9VvU3q4/MzN2rX6h+rsNKmodWr7oF7/+11RXVPav7VldX31fdu/q26qdm5tIj4/969Y3VH6vusse0/zL9s9U/rS6tPr96w37NVdWf2NseWj2g+l9OM59v2D//bfU51WdWz1xr/cFa6zP3mIevtU4bHDp8l7+3j3MqP1s9bH+GX61+4qT9j62eWl1S/UH1K3vcJdVLqn+yP/Odqn9VvXF/pkdW3zIzj9rHeVr1x/fPo6rHn2HOZ+OvVD/Z4Vy9tHpm1Vrr66u3VV95qlVIa63vrH65T1yy93dOcewznaMnVzd0OK/3rZ5SrbOc8+M6hNB7VddW33+qQTPzX1bPqr6+w+/xfarLjgz5o+pbO5yDP9vhu/6f9uf7C3vMidVYL+rw/7Q/1mEF2oOq329/XzPzGdWPVF++1rp7h/8G37D3PWZ/vr+6P+8vVy88w/sAwB2eUAQAt61Lqt9da33sFPvesfdX/a3qqWut/3cdvHGt9Z7q0dU711r/61rro2utD661XncL3v9H1lrXr7V+v8PqpZevtV6+1vr4WuuV1TUd4tQJP7bW+v/2+Bd3CAt1CEi/sNZ64V4V9Z611htmZqonVt+61nrvWuuD1dM7/aVhX1f9k7XWm3cs+47qcadbHXQaq/qu6rvmFJfurbWeu7+nP6i+u3r4zNzjyJCfXmu9fq310eqnq4+utV6w7/fzourEiqI/U1261vqetdZ/3Pfm+d+PfLbHVt+/P/f1HeLEzfnVvWrmxM+jjuz7v/e5+aPqx6uHn+X3cUZncY7+sLpf9eB9bn95rXW2oein11r/dv9+/0Sf+H052VdXL1trvWafl++qPn5i5z4fr11rfWyvuPsX1V883Zvu37+fWmt9ZH+e7z9p/Merz5uZu6613rHWOnE54/9Q/cO11pv2nJ9eff6JVUUA8KlIKAKA29bvVpecJoTcb++vemB1qsutTrf9bF1/5PGDq685GiqqP7/nccI7jzz+SIcVP2eax6XV3arXHznmz+3tp3L/6q1Hnr+1OtZhJctZW2u9vMMqmL99dPscLuG7ag6X8P1edd3edcmRYe868vj3T/H8xGd+cHX/k76vpxyZ6/276fd79HOdzheste555Ofnj+w7+bv/9FsY0E7n5s7RP+6wGugV+3KtK2/BsU/3+3Kym3xXa60PV+858Xxm/sS+fOyd+7w9vZues5uYmbvNzL/Yl7L9XvWa6p4zc+d97L/WIQq9Y18a96f2Sx9c/fCR7+G91XRYYQUAn5KEIgC4bf1Kh8ub/urRjTPzmdWX94nLyK7vcAnTya7vcInWqXy4QwA44bNPMeboypDrqx8/KVR8xlrrqpv/GKed3+92iCufe+SY9zhyGdnJ/kOHv6yf8KDqY9001pyt7+wQbo5+B3+9ekz1JR1uHH58b59zOP711VtO+r7uvtY6sQLrHR0C2gkPOof3OFs3t8LnTPvPeI726qsnr7U+p8Plb0+amUdemGn/Jzf5rmbmbh0uPzvhWdVvVw9ba31Wh/N6pnP25OpPVl+0x5+4bGyq1lo/v9b60g4R9Lc7rASrwzn92yed07uutf7NeX9CAPgkJRQBwG1orfWBDvdw+acz82Uz81/MzPEOl3Xd0OESo6ofrb53Zh42B//VzNynell1v5n5ln1D4rvPzBft17yh+oqZuffMfHb1LTcznf+j+sqZedReefPpc7gB9mU387o6XFb0JTPz2DncQPk+M/P5a62Pd/hL+DNm5o9VzcwDTrqk6qgXVt86Mw/Zsezp1YtOc2neGa21Xl39Rje9N9DdO4S593QISE+/pcc94t9WH5zDDcHvur+zz5uZEzetfnH1HfvGypdV33we73Vz3tXpg+EZ99/cOZqZR8/MQ/clah/ocL+gj5/qWOfhJdWj53BD9LtU39NN/7/07h3uO/Whvfrnfzzp9Sd/vrt3iF/vn5l7d7hfVPvz3HdmHrPvVfQH1YeOfJ5/3uGcfe4ee4+Z+ZozvA8A3OEJRQBwG9s3H35K9YMd/jL8ug4rGx6579dShxsov7h6xR7znOqu+/4rX1p9ZYfLfH6nw42g6xCZ3tjh8qpXdLi/zpnmcX2H1TZPqW7cc/h7ncX/H6y13tbhXkZP7nC5zhv6xD10vr3DpUuv3ZcB/UKH1R6n8tw979dUb6k+2vkFlqd2uDH3CS/ocAnY26vfql57rgfe9wp6dIf77rylw8qcH+2wUqkOAfCte98r+kT0O5MT/6LWiZ8fOsvp/MPqqfuSqW87xf4frr56/0tep7pX0pnO0cP28w91WAH3z9Zav3SW8zor+x5B31T9nx1WF72vQyg94ds6rAb7YIeodfLv8ndXz9+f/7HVD1V37XBOXtvhUroT7tTh5vD/ocPv6l9sh6e11k9XP1D95P4efqPDyr7TvQ8A3OHN2d+bEAAAAIA7MiuKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKjq2MWewJlccskl6/jx4xd7GgAAAAB3GK9//et/d6116an23a5D0fHjx7vmmmsu9jQAAAAA7jBm5q2n2+fSMwAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACA7djFnsC5OH7l1Wc99rqrrrgVZwIAAABwx2FFEQAAAACVUAQAAADAJhQBAAAAUAlFAAAAAGxCEQAAAACVUAQAAADAJhQBAAAAUAlFAAAAAGxCEQAAAACVUAQAAADAJhQBAAAAUAlFAAAAAGxCEQAAAACVUAQAAADAJhQBAAAAUAlFAAAAAGxCEQAAAACVUAQAAADAJhQBAAAAUAlFAAAAAGxCEQAAAACVUAQAAADAJhQBAAAAUAlFAAAAAGxCEQAAAACVUAQAAADAJhQBAAAAUAlFAAAAAGxCEQAAAACVUAQAAADAJhQBAAAAUAlFAAAAAGxCEQAAAACVUAQAAADAdrOhaGaeOzPvnpnfOLLt3jPzypn5nf3nvfb2mZkfmZlrZ+bXZ+YLjrzm8Xv878zM42+djwMAAADAuTqbFUXPq77spG1XVq9aaz2setV+XvXl1cP2zxOrZ9UhLFVPq76o+sLqaSfiEgAAAAC3DzcbitZar6nee9Lmx1TP34+fX33Vke0vWAevre45M/erHlW9cq313rXW+6pX9p/HJwAAAAAuonO9R9F911rv2I/fWd13P35Adf2RcTfsbafbDgAAAMDtxHnfzHqttap1AeZS1cw8cWaumZlrbrzxxgt1WAAAAABuxrmGonftS8raf757b3979cAj4y7b2063/T+z1nr2Wuvytdbll1566TlODwAAAIBb6lxD0UurE/9y2eOrnzmy/W/sf/3sEdUH9iVqP1/95Zm5176J9V/e2wAAAAC4nTh2cwNm5oXVF1eXzMwNHf71squqF8/ME6q3Vo/dw19efUV1bfWR6hur1lrvnZnvrf6fPe571lon3yAbAAAAgIvoZkPRWutrT7PrkacYu6pvOs1xnls99xbNDgAAAIDbzHnfzBoAAACAOwahCAAAAIBKKAIAAABgE4oAAAAAqIQiAAAAADahCAAAAIBKKAIAAABgE4oAAAAAqOrYxZ7A7cnxK68+67HXXXXFrTgTAAAAgNueFUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVHXsYk/gU8XxK68+67HXXXXFrTgTAAAAgFOzoggAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACA6jxD0cx868z85sz8xsy8cGY+fWYeMjOvm5lrZ+ZFM3OXPfbT9vNr9/7jF+IDAAAAAHBhnHMompkHVH+3unyt9XnVnavHVT9QPWOt9dDqfdUT9kueUL1vb3/GHgcAAADA7cT5Xnp2rLrrzByr7la9o/pL1Uv2/udXX7UfP2Y/b+9/5MzMeb4/AAAAABfIOYeitdbbqx+s3tYhEH2gen31/rXWx/awG6oH7McPqK7fr/3YHn+fc31/AAAAAC6s87n07F4dVgk9pLp/9RnVl53vhGbmiTNzzcxcc+ONN57v4QAAAAA4S+dz6dmXVG9Za9241vrD6l9Wf666574Ureqy6u378durB1bt/feo3nPyQddaz15rXb7WuvzSSy89j+kBAAAAcEucTyh6W/WImbnbvtfQI6vfqn6p+uo95vHVz+zHL93P2/t/ca21zuP9AQAAALiAzuceRa/rcFPqX63+3T7Ws6tvr540M9d2uAfRc/ZLnlPdZ29/UnXlecwbAAAAgAvs2M0POb211tOqp520+c3VF55i7Eerrzmf9wMAAADg1nM+l54BAAAAcAciFAEAAABQCUUAAAAAbEIRAAAAAJVQBAAAAMAmFAEAAABQCUUAAAAAbEIRAAAAAJVQBAAAAMAmFAEAAABQCUUAAAAAbEIRAAAAAJVQBAAAAMAmFAEAAABQCUUAAAAAbEIRAAAAAJVQBAAAAMAmFAEAAABQCUUAAAAAbEIRAAAAAJVQBAAAAMAmFAEAAABQCUUAAAAAbEIRAAAAAJVQBAAAAMAmFAEAAABQCUUAAAAAbEIRAAAAAJVQBAAAAMAmFAEAAABQCUUAAAAAbEIRAAAAAFUdu9gT4Pwcv/Lqsx573VVX3IozAQAAAD7ZWVEEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUdexiT4Dbp+NXXn3WY6+76opbcSYAAADAbcWKIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiOXewJ8Knn+JVXn/XY66664lacCQAAAHCUFUUAAAAAVEIRAAAAAJtQBAAAAEAlFAEAAACwCUUAAAAAVEIRAAAAANt5haKZuefMvGRmfntm3jQzf3Zm7j0zr5yZ39l/3muPnZn5kZm5dmZ+fWa+4MJ8BAAAAAAuhPNdUfTD1c+ttf5U9fDqTdWV1avWWg+rXrWfV3159bD988TqWef53gAAAABcQOccimbmHtVfqJ5Ttdb6j2ut91ePqZ6/hz2/+qr9+DHVC9bBa6t7zsz9znnmAAAAAFxQ57Oi6CHVjdWPzcyvzcyPzsxnVPdda71jj3lndd/9+AHV9Udef8PeBgAAAMDtwPmEomPVF1TPWmv96erDfeIys6rWWqtat+SgM/PEmblmZq658cYbz2N6AAAAANwS5xOKbqhuWGu9bj9/SYdw9K4Tl5TtP9+997+9euCR11+2t93EWuvZa63L11qXX3rppecxPQAAAABuiXMORWutd1bXz8yf3JseWf1W9dLq8Xvb46uf2Y9fWv2N/a+fPaL6wJFL1AAAAAC4yI6d5+u/ufqJmblL9ebqGzvEpxfPzBOqt1aP3WNfXn1FdW31kT0WAAAAgNuJ8wpFa603VJefYtcjTzF2Vd90Pu8HAAAAwK3nfO5RBAAAAMAdiFAEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAdu9gTgAvl+JVXn/XY66664lacCQAAAHxysqIIAAAAgMqKIrhZVioBAADwqcKKIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoLoAoWhm7jwzvzYzL9vPHzIzr5uZa2fmRTNzl7390/bza/f+4+f73gAAAABcOBdiRdH/XL3pyPMfqJ6x1npo9b7qCXv7E6r37e3P2OMAAAAAuJ04r1A0M5dVV1Q/up9P9Zeql+whz6++aj9+zH7e3v/IPR4AAACA24HzXVH0Q9Xfrz6+n9+nev9a62P7+Q3VA/bjB1TXV+39H9jjAQAAALgdOOdQNDOPrt691nr9BZxPM/PEmblmZq658cYbL+ShAQAAADiD81lR9OeqvzIz11U/2eGSsx+u7jkzx/aYy6q378dvrx5Ytfffo3rPyQddaz17rXX5WuvySy+99DymBwAAAMAtcc6haK31HWuty9Zax6vHVb+41vq66peqr97DHl/9zH780v28vf8X11rrXN8fAAAAgAvrQvyrZyf79upJM3Nth3sQPWdvf051n739SdWVt8J7AwAAAHCOjt38kJu31np19er9+M3VF55izEerr7kQ7wcAAADAhXdrrCgCAAAA4JOQUAQAAABAJRQBAAAAsAlFAAAAAFRCEQAAAACbUAQAAABAJRQBAAAAsAlFAAAAAFRCEQAAAACbUAQAAABAJRQBAAAAsAlFAAAAAFRCEQAAAACbUAQAAABAJRQBAAAAsAlFAAAAAFRCEQAAAACbUAQAAABAJRQBAAAAsAlFAAAAAFRCEQAAAACbUAQAAABAJRQBAAAAsAlFAAAAAFRCEQAAAACbUAQAAABAJRQBAAAAsAlFAAAAAFRCEQAAAACbUAQAAABAJRQBAAAAsAlFAAAAAFRCEQAAAACbUAQAAABAJRQBAAAAsAlFAAAAAFRCEQAAAADbsYs9AfhUdfzKq2/R+OuuuuJWmgkAAAAcWFEEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAdu9gTAC6841defdZjr7vqiltxJgAAAHwysaIIAAAAgEooAgAAAGATigAAAACohCIAAPurYyQAACAASURBVAAANqEIAAAAgEooAgAAAGATigAAAACohCIAAAAANqEIAAAAgEooAgAAAGATigAAAACohCIAAAAANqEIAAAAgEooAgAAAGATigAAAACohCIAAAAANqEIAAAAgEooAgAAAGATigAAAACohCIAAAAANqEIAAAAgEooAgAAAGATigAAAACohCIAAAAANqEIAAAAgEooAgAAAGATigAAAACohCIAAAAANqEIAAAAgEooAgAAAGATigAAAACohCIAAAAAtmMXewLAJ4/jV1591mOvu+qKW3EmAAAA3BqsKAIAAACgEooAAAAA2IQiAAAAACqhCAAAAIBNKAIAAACgEooAAAAA2IQiAAAAAKo6drEnAHD8yqtv0fjrrrriVpoJAADApzYrigAA/n/27jze9nLu//jrfSoqKRHdhiZRZIiUkgyJn6GBbpGUksxThhvdyDyVOUMpJJQxhELpbo5GFZVuZMzs1qCQ+Pz++Fzfs9feZ59hr+u6ztlL7+fjcR7n7LX3/qxvq/X9ruv7uT7X5zIzMzMzwIkiMzMzMzMzMzMrnCgyMzMzMzMzMzPAPYrM7N/cXPofufeRmZmZmZnd3LmiyMzMzMzMzMzMACeKzMzMzMzMzMyscKLIzMzMzMzMzMwAJ4rMzMzMzMzMzKxwosjMzMzMzMzMzAAniszMzMzMzMzMrHCiyMzMzMzMzMzMACeKzMzMzMzMzMysWHlFH4CZ2STa8IDjl/lnf/aOHTseiZmZmZmZWTuuKDIzMzMzMzMzM6AiUSRpPUmnSLpM0qWS9i+P31bSSZJ+VP5euzwuSYdI+rGkSyRt0eo/wszMzMzMzMzM6tVUFN0EvDwiNgO2AV4gaTPgAODkiLg7cHL5GuCxwN3Ln2cDh1Y8t5mZmZmZmZmZNTZ2oigifhMRF5Z/XwdcDtwZeDxwVPmxo4AnlH8/HvhkpO8Ct5F0x7GP3MzMzMzMzMzMmmrSo0jShsD9gXOAdSPiN+VbvwXWLf++M/DLkV/7VXnMzMzMzMzMzMzmgepEkaQ1gGOBl0TEtaPfi4gAYo7xni3pfEnn/+EPf6g9PDMzMzMzMzMzW0Yr1/yypFXIJNHREfGl8vDvJN0xIn5Tlpb9vjx+FbDeyK/fpTw2TUQcDhwOsOWWW84pyWRmNuk2POD4Of38z96xY6cjMTMzMzOzm6OaXc8EfAy4PCLeM/KtrwL7lH/vAxw38vjeZfezbYBrRpaomZmZmZmZmZnZClZTUfRg4GnA9yVdVB57NfAO4POS9gN+Djy5fO8E4HHAj4EbgH0rntvMzMzMzMzMzBobO1EUEWcCWsy3d5jl5wN4wbjPZ2ZmZmZmZmZmfTXZ9czMzMzMzMzMzCafE0VmZmZmZmZmZgY4UWRmZmZmZmZmZoUTRWZmZmZmZmZmBjhRZGZmZmZmZmZmhRNFZmZmZmZmZmYGOFFkZmZmZmZmZmaFE0VmZmZmZmZmZgbAyiv6AMzMbPnY8IDjl/lnf/aOHTseiZmZmZmZzVeuKDIzMzMzMzMzM8CJIjMzMzMzMzMzK5woMjMzMzMzMzMzwIkiMzMzMzMzMzMr3MzazMyquEm2mZmZmdm/D1cUmZmZmZmZmZkZ4ESRmZmZmZmZmZkVThSZmZmZmZmZmRngRJGZmZmZmZmZmRVOFJmZmZmZmZmZGeBEkZmZmZmZmZmZFU4UmZmZmZmZmZkZ4ESRmZmZmZmZmZkVThSZmZmZmZmZmRngRJGZmZmZmZmZmRVOFJmZmZmZmZmZGeBEkZmZmZmZmZmZFU4UmZmZmZmZmZkZACuv6AMwMzObzYYHHD+nn//ZO3bsdCRmZmZmZjcfrigyMzMzMzMzMzPAiSIzMzMzMzMzMyucKDIzMzMzMzMzM8CJIjMzMzMzMzMzK5woMjMzMzMzMzMzwLuemZnZzdBcdlSby25qveKamZmZmS0vThSZmZnNc05AmZmZmdny4kSRmZnZzZirq8zMzMxslHsUmZmZmZmZmZkZ4ESRmZmZmZmZmZkVXnpmZmZmE8NL2szMzMz6cqLIzMzMDCehzMzMzMBLz8zMzMzMzMzMrHCiyMzMzMzMzMzMACeKzMzMzMzMzMyscKLIzMzMzMzMzMwAJ4rMzMzMzMzMzKxwosjMzMzMzMzMzAAniszMzMzMzMzMrFh5RR+AmZmZ2b+zDQ84fpl/9mfv2LHjkZiZmZktnRNFZmZmZhPICSgzMzPrwYkiMzMzM5vGSSgzM7ObLyeKzMzMzGy5cALKzMxs/nMzazMzMzMzMzMzA5woMjMzMzMzMzOzwkvPzMzMzGyieUmbmZlZO04UmZmZmZkthpNQZmZ2c+NEkZmZmZnZcuYElJmZzVfuUWRmZmZmZmZmZoAriszMzMzM/m24UsnMzGq5osjMzMzMzMzMzABXFJmZmZmZ2VLMpVIJXK1kZjbJXFFkZmZmZmZmZmaAE0VmZmZmZmZmZlY4UWRmZmZmZmZmZoATRWZmZmZmZmZmVriZtZmZmZmZrTBzaZQ9lybZveKamf27c0WRmZmZmZmZmZkBrigyMzMzMzNbZnOpVAJXQZnZ5HGiyMzMzMzM7N+YE1BmNhdeemZmZmZmZmZmZoATRWZmZmZmZmZmVnjpmZmZmZmZmc1Zz35NZrbiOFFkZmZmZmZm80qvvkrzIe5cY5stb04UmZmZmZmZmc1T8yG55cTWzYsTRWZmZmZmZmbWRM8ElJNby4cTRWZmZmZmZmZ2s+UE1HROFJmZmZmZmZmZNTap1VUL5vTTZmZmZmZmZmb2b8uJIjMzMzMzMzMzA5woMjMzMzMzMzOzwokiMzMzMzMzMzMDnCgyMzMzMzMzM7PCiSIzMzMzMzMzMwOcKDIzMzMzMzMzs8KJIjMzMzMzMzMzA5woMjMzMzMzMzOzwokiMzMzMzMzMzMDnCgyMzMzMzMzM7PCiSIzMzMzMzMzMwOcKDIzMzMzMzMzs8KJIjMzMzMzMzMzA5woMjMzMzMzMzOzwokiMzMzMzMzMzMDnCgyMzMzMzMzM7PCiSIzMzMzMzMzMwOcKDIzMzMzMzMzs8KJIjMzMzMzMzMzA5woMjMzMzMzMzOzwokiMzMzMzMzMzMDnCgyMzMzMzMzM7PCiSIzMzMzMzMzMwOcKDIzMzMzMzMzs8KJIjMzMzMzMzMzA5woMjMzMzMzMzOzwokiMzMzMzMzMzMDnCgyMzMzMzMzM7PCiSIzMzMzMzMzMwOcKDIzMzMzMzMzs8KJIjMzMzMzMzMzA5woMjMzMzMzMzOzwokiMzMzMzMzMzMDnCgyMzMzMzMzM7PCiSIzMzMzMzMzMwOcKDIzMzMzMzMzs8KJIjMzMzMzMzMzA5woMjMzMzMzMzOzwokiMzMzMzMzMzMDnCgyMzMzMzMzM7PCiSIzMzMzMzMzMwOcKDIzMzMzMzMzs8KJIjMzMzMzMzMzA5woMjMzMzMzMzOzwokiMzMzMzMzMzMDnCgyMzMzMzMzM7PCiSIzMzMzMzMzMwOcKDIzMzMzMzMzs8KJIjMzMzMzMzMzA5woMjMzMzMzMzOzwokiMzMzMzMzMzMDVkCiSNJjJF0h6ceSDljez29mZmZmZmZmZrNbrokiSSsBHwIeC2wG7CFps+V5DGZmZmZmZmZmNrvlXVH0QODHEXFlRNwIfBZ4/HI+BjMzMzMzMzMzm8XyThTdGfjlyNe/Ko+ZmZmZmZmZmdkKpohYfk8m7QY8JiKeWb5+GrB1RLxw5GeeDTy7fLkpcMUyhl8H+GPDw10esSctbs/Ykxa3Z+xJi9sz9qTF7Rl70uL2jD1pcXvGnrS4PWM7bv/Ykxa3Z+xJi9sz9qTF7Rl70uL2jD1pcXvGnrS4PWNPWtyesedD3A0i4vazfWPldsezTK4C1hv5+i7lsYUi4nDg8LkGlnR+RGxZd3jLN/akxe0Ze9Li9ow9aXF7xp60uD1jT1rcnrEnLW7P2JMWt2dsx+0fe9Li9ow9aXF7xp60uD1jT1rcnrEnLW7P2JMWt2fsSYvbM/Z8j7u8l56dB9xd0kaSbgE8Bfjqcj4GMzMzMzMzMzObxXKtKIqImyS9EPgWsBLw8Yi4dHkeg5mZmZmZmZmZzW55Lz0jIk4ATugQes7L1eZB7EmL2zP2pMXtGXvS4vaMPWlxe8aetLg9Y09a3J6xJy1uz9iO2z/2pMXtGXvS4vaMPWlxe8aetLg9Y09a3J6xJy1uz9iTFrdn7Hkdd7k2szYzMzMzMzMzs/lrefcoMjMzMzMzMzOzecqJIjMzMzMzMzMzA5woWq4krSLpxZK+WP68SNIqK/q4zGz+kbSqpJdJ+pKkYyW9VNKqK/q4VgRJt5K0oPx7E0m7+NppknYe3hc2RdICSWuu6ONYEkm3neWxjVbEsfw7k/RgSbcq/95L0nskbbCij8v+PUl6t6R7rejjuDmQtIakNVb0ccwXktaWdN8VfRz/bjzAmkHSSpJ+2Cn8ocADgA+XP1uUx27WJK2+oo9hriZhID6Jet34lfP66NZxS+wnLctjY/gkcC/gA8AHgc2ATzWIC4Ck7STtW/59+3l+k3Y6sKqkOwMnAk8DPtEisKSNRhNwklaTtGGDuCvVxlhBsf+z3Ey+W9KujWIu8t5q9H7bHfiRpIMl3aNBPKDvzY6kk5flsTHiHiNpzZIU+AFwmaRX1Mad8RwtP6u/NvoZKmkz4Gu1QYf3gaQtZvvTIH6Xc28xx7uxpNpNZw4FbpC0OfBy4CfkZ0u1xZzXWzWKfQtJ9y5/mk0KSNpA0iPLv1eTdOtGcTeb5bGHN4jb5drZ8Rp3OXC4pHMkPVfSWq0Cd74uby7pheXP5j2eoxVJ95H0PeBS8jp/gaR7N4rd6/PpS5J2bD2+l3Rq+dy7LXAhcISk9zSMv66kncqfO7SKW2JvK+mpkvYe/jSIubqkAyUdUb6+u6SdqmJOcjNrSQ8G3gBsQO7gJiAi4q6VcY8DXhQRv6g+yOlxL46IzZf2WEX8Hckby4U3PRHxpgZxbw88C9iQkZ3yIuIZlXG3BT4KrBER65eL83Mi4vmVcVcF9mPR16L2eI8Bngv8EzgPWBN4f0S8szJul/dxif01YOZJfg1wPvCRiPhbg3gLRcQucz7I6fE/DTwIOBb4eEQ0S9pKOhN4RETc2CpmiXthRGyxtMfGiHtZRGy2tMfGjP16YEtg04jYRNKdgC9ExIMr494SeCKLXiuqrkPD6ynpRcBqEXGwpIsi4n41cUvs84Fth/eFpFsAZ0VE1Q2PpCvJ9/GREXFZ7XEuj9iSPgzcDfhMeWh34CcR8YLKuLOdIxdExANq4pY4awJ7APuS16Yjgc9ExHUVMZ9Z4q08Eu+ayuNcFVgdOAV4OHmdh/wc+WZEVCW6hvNB0p7kJNQBwAURUT3D2uOzuoxXXgnsCGxKJi/2jIiLKo/18Ih4tqRTZvl2RMQjKuP3Ove+S/5/u4R8b9ybvBFcC3heRJw4Ztzh2vk64KqI+FiLz6chNrBzRFxVvn4Y8MGIuE9l3IcDRwE/I1+L9YB9IuL0yrjPAp4N3DYiNpZ0d+CwiNihJm6J/QNyIudgctx5MLBlRDyoMm6Xa2ePa9yM+JuW+HsAZwFHRMRs5+RcYnY5Zkn7k/c4XyoP7QocHhEfaBC7+fhe0tnAa4bXs5wvb4uIbSti9v58eiT5/24b4Avk9fOKmpgl7vci4v7lvbFeRLxe0iWNPveeDLwTOJV8PR4CvCIivtgg9qeAjYGLyHtKyPfFiyvjfg64ANg7Iu6tnNw5u2asXDtTsaJ9DHgp+aL8cyk/OxdrA5dKOhe4fniw9iYY+KekjSPiJwCS7kqj45Z0GHmSb08O6HYDzm0RGzgOOAP4Nm1f5/cCjwa+ChARF0t6aIO4nwJ+WGK/CdiTnOWotVlEXFsG4t+gDMTJC0mNXu9jgCuB2zP9xu86YBPgCLIyYy7eVf7+T+A/gE+Xr/cAfld1pEBE7DVy4/cJSU1u/IorgbMkfZXp5/VYsw+SHgs8DrizpENGvrUmcFPNgRYXStomIr5bnm9rMsHXwq7A/ckZGCLi141mVo8jE5EXAH9vEG8gSQ8iz+X9ymOtZvZXHk0eRsSNJVlUa3PgKcBHyyzax4HPRsS18zj2I4B7RplBknQUebM6FmV1x72AtST958i31mQkiV+jXJO/CKwGvIR8b79C0iHjDvQj4qPkazvc7FwiqfZm5znl+O5Enh8iE1vXkVWDtVZRVl48gbxZ/0e5frbQ/LM6Io4vx3sicGtg14j439oDjYhnl7+3r421GL3OvV8D+0XEpbCwQuVNZDLtS+TrNI7rJP03sBfw0HLMrSp0ngN8RdLOZJLr7eRnYq13A/9vuJGUtAk5hqlNLL8AeCBwDkBE/KhhlcDWwEHA2eT7+Whg7ImX3tfOTtc4YGHV3T3Knz8CFwMvk/SciHjKPDzm/YCtI+L6cvwHAd+hzXW5x/j+VqP/vRFxqsry0gqzfT4BXEtWtFeJiG8D31ZWmO1R/v1L8j7k0xHxjzFDryzpjsCTgdfUHucMrwG2iojfw8KiiW8D1YkicqJ2s2Gs1dDGEbG7pD0AIuIGSVraLy3JpCeKromIb3SIe2CHmACvAE4pM1IiM8z7Noq9bUTct2RS3yjp3WQyo4XVI+JVjWJNExG/nPEebnEhvVtEPEnS4yPiqFIJdEaDuL0G4r3ex5Dvi9HKiK9JOi8itpI05xvAiDgNsgQ4IracEbdJEqPHjV/xk/JnATmQq/VrMnGzC/nBOriOHBjUegBwtqShsnF94ApJ3ydnHmpmTG6MiBjevw0GGYO7RMRjGsUa9RLgv4EvR8SlJcleNZgd8QdJu0TEVwEkPZ4c3FYpic0jyFLohwHHAO8t7+03R8SP52HsH5Pvs5+Xr9crj41rU2An4DbAziOPX0fO4FaRtAv5GXo3siLlgRHx+zKLdhkVA/3WNzsR8X7g/aWy433lOncgeYP9nXGPc8RHyAqMi4HTlX1oWiQlgXaf1ZI+wPSq1LXI6/ILJVE7ozryPCuR1UobMr26sWpZQsdzb5MhSVSe5zJJ94iIKyvH+bsDTyWTUL+VtD71k1vDMZ4n6cVkEutvwCMj4g8NQq8yWm0QEf+rNsvP/l4mAgBQLutrdaP2D+Cv5LhlVeCnEfGvinhdr53QJ6Ej6b3k8Z5MVrkME9YHSWpRQdIjCSWmX8/+yVSipFaP8f2V5bNjaEWwFzkROraRz6cXtaikmo2k25HH+jTge2QydTtgH7KKaRxvAr5FVoGfV8aGP6o/WgAWDEmi4k+0a9nzA3Ky/TeN4g1ulLQa5bomaWMqJ20nPVF0iqR3krMtC1+IiLiwJmhEnFYGWXePiG+XQWf17HVEnKwsdd20PHRFRLSadf9r+fsG5TKSPwF3bBT765IeFxEnNIo3+KWypD3KIGB/2lT+DJnpq5Xrdn8LtJg16jUQ7/I+LtaQtH6UZZRlkDg0v6tZgnUrSXeNiCtL3I2A6mRDzxu/iHhjeY7VI+KG2mONiIuBiyUdM8yGSFqbLH/9c218oEfCZfB5SR8BbqMsx38GefNT62xJ94mI7zeItVBJUA5JygXAH1vdUJLLSY+WNMya/Yq5V9otYuRGdV/yZvXd5MDoIcAJZFXffIt9a+ByZTUtwFbA+coqvDlX1UbEccBxkh4UES2SITM9EXhvzFiSUmbR9lvM7yxVudnZCfgf2t/s7BYRb5K0HVnB9S6yj8zWNUEj4hBgtLLx55JaVdW0/KyeOaFwwaw/Ve9rZPLi+0DNDfs0Hc+9SyUdCny2fL072X/klkyNaeYsIn4LvGfk619Q2aNIiy5BX52sJP1YSfbVVt+fL+mjTFUs70mbatrTJL0aWE3So4Dn06AvVnEeWVG7FbAOcJikJ0bEWP0Ke187O17jLgFeO1TnzPDAirg9j/lI4BxJXy5fP4GsFGyhx/j+GcAbmVoqd0Z5rIU7SFopIv4JC5d2vz8iqooaymu7KZnc2jkihgTJ52ommSPiC+RStuHrK8lxQQvflPQtpq/IaHUfvA55fT+X6e+L2mvn64FvAusp+7I+GHh6TcBJ71HUaw16l3XMkl4AHB0RV5ev1wb2iIgP18QtsQ4kb6J3AD5Efoh/NCLGro6SdF2JIzIJ8HdywDKssa1q5ixpHeD9wCNLzBOB/SPiT5Vxn0n2ELgv+QGwBvC6iDisJu5inmvliKhaZtTrfVxiPw44jJyxFbAROTg6FXhWRLxvzLiPAQ4nZzGG6rjnRMS3Ko/3KOBjM2/8yvd2iIixm+oply59jPY9sU4lq4pWJm96fk+uCR6rqkjSmqXaYJFdgQAi4v/GPdYSX8BdyBm5/0f+//tWRJxUE7fEvoxM8v2UvF4M14qq9eLq0B9M0stmPDQ06r0B6isPlJWjp5Dv57NnfO+QmkRXr9ilQmKxhorCMeIeDLyFnND4JnltfmlEfHqJv7iCKJu8f362mx1Ja0VFXwxN9VR4O/D9iDhmeKzymNcF3gbcKSIeq1y69KCI+FhN3BK76Wd1SbZ8MiL2rD22JTxHkz4Vs8Ttde6tRn42b1ceOovc9ORvZFX3X8aMuw05NrwncAty0vMvETF2k+Fe14mR+Lckl4kNr8UZwIdrJ1bLJMN+TP/cazFBgqQtI+L8GY89LSKqNqDode1sfY3TUhrFt5j47Hxd3oKR91tEfG/cWDPidhvf9yDpbeQy432BdcllZx+IiKrlZ5K2j8oljYuJuwk50bJuZE+e+wK7RMRbGsV/IlNLSM+IiC8v6efnEHfWa2jttbPEvh3ZC0rAdyOiqkJ+ohNFvUi6iLKOeRi8Sfp+1DfoW6T5aosBYolzy+FDtHzIrgr8rWHFUlPLY6DYgzo1DO+pvB+GRnRXxBwbWC9j3B/O1/faQNI5ZO+ur46c1z+IiKrdItS4mZ6kr0fETpJ+ylSidhDRpsl59fVsMXFn3XY5In4+2+NziNu8Ua+yoTfkLNdW5GywyLL5cyNir8pjXmPcm7sVHHtd8vWAfB1+v6SfX8aYw/+/XckZ4ZcBp0flRg6LuQm+vnYSo8ReG7g706/1Vc10S9yvA1cBjyLfy38lX+fa1+Ib5MTIayJic+Wymu/1OM9bUKfNBUbiHwScHGM2gV5C3G7nXg9ltv4p5Kz7lsDe5DK3/66MuxLw7ejXC6o5SftHLrFZ4mNzjNl7YqfLtbPEbnaNW0xCZCRsm8RIj+uypE9FxNOW9th8ocab1MwSfwfg68CfgYdGxTJ5Te+vtYiI+NKSvr8M8U8j27p8pOW4fnloOdbqmaid6KVnyqZYrweGpoqnAW+qySoXvdYxryRJEQsbha5EDm5b+A456KTcsP9duSNFi50tZotxDfDzcatpIuKfym1Kb9FqoChpr4j49CyVAsNz1lYIdGkY3vF9PHgAU30aNleWhrfYHrd53J43ftCtJ1bTZnolSSTgYdF458URF0raKiLOaxk0In5eKrUeUh46I3KJXq3m/cFiaini6cAWUZqlS3oDcHzl8QLcpKwibbr7Ys/YWnSXjw9IarHLx9BfZEdyd71rVNd3ZfBBZrkJrg1akr77k5V3F5Gzc98hl4rVejK5rPRdEXF1uXa02MZ+nYj4vLJxMRFxk6RWm2X0mLVturnALL4LfLlUkTSrhKbfuTdzd6QhbvXEQET8WFPLSY5Ubq1dlSgqY7h/1VZyzEalD9+Mh4cb4beMW8lG9kKZmRR6+iyPzcUxZALnAmaZ2AFq//91uXa2vsYtj4Rhx+vyvWY8z0rUN04fYvUY37fepGYh5SYFh5C9f+5DjgH2i4hfjxly5yV8L5haPjeu1SPi3BnnRO0qj2E1zawaTUS1Hmu9ewnfCyrOkYlOFJFrSH9ADrwgT44jyR2ZapymPuuYv0mux/xI+fo55bGxSfoP4M7ksd4fpm1puPpif3FuPkwmnIa+I/chX/e1JI29bSvtB4pDj5wWjYpn06theK/3MVrMFozU9yjoEpdON35Fr55YzZvpRURIOp4813rYGthT0s/Jc6/VErGZ28x+WrlddW1zxJ6Netdler+uG8tjtXrtvtgzdq9dPr4m6Ydk9czzStwmlY09boLJa8NWZNn29sodiN5We6yQ/ZMYGRxH9mpo0dDyemXJ+TARtQ15c93CEZRZW4CIuES5HLQmUdR6c4GZ3gM8iFze17J0vte512v30xuUuzhepFzG9BvaNWT9C/B9SScxfQxX2z/uG+RrcEz5+inkePa3wCdY8s3nIpQ7AD0V2KiMNwe3BqoqfiJip/L3RjVxlqDXtbPLNU7S3rM93mhysukxl6T6cK93LVP3TzeSrRVa6DG+b7pJzQzvAp4UEZfBwoqg/2Fq9cCcRMS+JVm/W0R8vvLYZvNHZcPm4XNvNyo/TyPi1iXWm0usT5HvjT1p1/u36VhrSNRKWnVmRZmkql0SJ3rpmWZfyrXIY2PEXWQdM9nvp+rFKnGfTa7zBzipxB17UCBpH3JGZEumN/u7DvhEbVlfeY4vAQfGYrZtHff11tTSj2mGmf75RtI5EbG1pO+SF/k/AZdGxN0q43Z5H5c4l9NhC8aOcc+PiC01snRL7ZZnztZn48W1peG9KPs1fbB11U+J3WuJ2CVkX5Rhm9lbAd+pTUAt5rmq+4OVOK8hB3GjjSw/FxFvr4w7LEm8pCSYVyErrLapPORusTVjSWL5zLo4GixfUi7NuKZUItwKuHVko92amKeT5/NHyZvI3wBPj/plXMOg+yJy2+S/S7o0Iu611F9eQZSVvx8A7k3emNyeHJxf0iD28HosvBY3/IxaAyAaL+cq742HR93OU7PF7XXunRMRVQ3NFxN3A7Jv3ipkImotst9PzW6GQ+x9Zns8Io6qjHthRGwx22Mzr1HLGG8DYCPg7eSy5cF1wCUtPkfK89yZRSvCWixXHb12rg6s2eDa2eUap9zVcLAq2Tf1wojYrSZubOjbFQAAIABJREFUid3rmN8elUsxlxC7+fi+jL8fHdM3qflWRNyzdrw8Muky+tjtKqr4hhjnx/SdkpsoE7OHA9uSS+V+CuwVET9rEPvimWOJ2R4bM3aXsdaSrp3jxpz0iqK/StouIs6EhaW7f13K7yyLJ5D9c5o0uRuUActh5G4ItyW3kq6aOSofyEcpd1c4tsVxzqLLtq0xtfSj6UCxZGafxaLb4tYu+fi6pNuQ5YIXUhqGV8aEfu9j6LcFY6+4PWc/N40ZPbHKa31WTVD1a6bXpeoHui4R67bNrGbpD0YmrKtExFuV/V2G12LfaNPIstfuiz1jz7bLR3XVZLm5eT6wPjlZcieyN9TXK0M/jbw+vJC8CV6PNjue/Kpc678CnCTpz0BVErW3iLhQ2SBzU/KcuyLKbowNNJ+1Le/bTwG3LV//Edh7dKxR6Urg1HJuj+4qU7u0rde512sX3+F9+1dyp6RmahNCS7CSpAdG2dVK0lZM7Tw856ROeQ1+Lun0mNEsVtnL6lW1B1zi7E7uzjpaZV3bP2fvkX+Pfqu2QqfLNS4iXjT6dXmOzy7mx+eq1zH/d68kH33G9y8HzpQ0bZOaMgFTe05urNx9cdp4lrrqUYBvS/ov4HNMrz6srei7Enhk+W9fEKWFQCPXK/tifpY8l/dg5NgrNR1rqePqokmvKNqcvFgOuzf8GdindgZN0pHker7TyTf1NxvNXJ9Kw92RZsS+JTlA3pDpF7rqGylJnyerZ0a3bV2HHKSfGdNLIOcSd9pAEWgyUJR0NrlLxrQS7paJtPJ6rxoN1ub3eh+X2KcA9yN7KTXbgrFj3J6zn80z7SVGl2Z66lT1U2LPXCK2K1C9REzZH2wfsjpHwOPJysaxdtcbiTtrf7CIGHsL9N7UcffFzrH/k+m7v1Tv8iHpc+T1eO8y+Fyd/Oyrrf7docRplVif7TkeRl6HvhmdGi+3olxauyHTxwDVSz4WM2u7Z821qHxOvybKTjiSHk5ueb1t7fGWeF0qlnude+q3i2+vfj8odwR+O7AZ0/s1VfXlKYmhj5Ovrchlxs8ELgV2jDGXsCxmDNBkdzzl9uz3jcabevSs0Bl5jm7XOGXF3Q8iYtPGcZsds6R3kMsbpyX5asezJfb9yOTNWuR7+f9oc5/aa5OaXuPZn87ycDS4VvS8992QXIXwYPIaehbwkhbVSiV+s7GWpq8uOo+pRNG1wFFRsbpo0hNFG0XETyWtCRC588BGETHbG3KusVcBHksmRbYDToqIZ1bGbLo70ozY3yQ//GcmR5bU4GpZY/fatrXLQFGNSuJH4vXu2t/zfdxlC8ZecXuQ9CDyBuclwHtHvrUmsGttGak6LctQx5041HGJmKa2mQ0ykVxdnaOpZR7D32sA34iIhyz1l22ZSTooIl61tMfGiDssKR09R6pLuJXLMx9EDr7PICd3zoyIP48Zb9adiwa1s589aTF946K+XwySHhARF4zO2kraKSLGrgib7f9/i/eETaeszF1cv5/tImJO/X5mxD6TbNT7XrJv0L7k++N1VQc9FX8tgNoJOUnPI8ewGwOjk063JnsLVu1wWZ7jG2Rvl6474g0VOhHxmDF/v+s1TtN35FqJTCJ+vuYzZDkcc5ck34znWDi+bxBrdXL3uw0i4lklYbtpzfV4JHa3ZcY99Lz37anjWOuVEXHwjMeq7icnfenZseRuNaMn3hdp0K0+cledb5AXvNXI5WhViSIa7440w13G/eBYEmX3/xMiG2XNduLVfCjeakgSAUTEqWUgWuvrkh4XESc0iAX9u/b3fB93Sdy0jruYWc/R56tJYKxCzkyuzPSmqdeSlSm1mi/LKLrtxAH9loiVWFH+tOoPMlSM3CDpTmSFY6umgk1pMbsuDqJi6UvP2MWjWHQZxmNneWyubiwTDsM5sjEjlYjjioh9Srw7kefyh8hlbeOObUZ3LlqfrJ4RcBvgF2SJ/3y1JR36xhVHSNo7In4AIOkpZMVnzY3JlZIOJKuKAfYil4s1oVyC/koW3Z1srAqdXueeOu/WCjxyRhXN9zXV76c2QbJaRJwsSaW67A2SLgCqE0UaWWqssuSqokrgGHJZxyI9ihomf28gl8yfzPQq6+pE7QzXU3cd6n2NexdTY7mbyN2Rr6qM2fuYryTHic0TRZqx61mp2Knd9exI8jV5UPn6KnIDmOpEEf3Gs8MqkpnVh7UVr13ufWHhZ0iPNibQb6z1FODgGY9V3U9OZKJI2en+XuSuW6PVHmsyvX/FuPGHSqKHk1vXfZSpjvU1ht2RzoxGuyONOFvSfSLi+0v/0WUXHbdApfFAUVNbGgp4taQbmeonEDHmloYRse+4x7QkPd/Hks6MiO206DaPTbYIVvtt7Hcqf7+g/D36nqi98Xl9ROwg6V7Rp1H6C8hlGfeQdBVlWca4wbToThyQ/99a7sRxJHCOpNEGzh+rDaqpJW3HksfcatezXv3BehiSkZuSO7QMu+zsTC7VnHexR2bc71qqzUafr6qHV/F6cofP9SQdTZZyP702aLnRfQi5O+AfyV0Tzxg3XpSdiyQdAXx5mGwoY4In1B5vZ736xkEm4b4o6ank6703udlHjWeQPXO+RJ7PZ5THWjmabB2wE/BccknsHyri9Tqve+/W2rTfzwx/VzZh/ZGkF5I3rGtUxlzsUuNx45Wx6zXAHprRm4/KXc9GfJWp90QzMyp0FlAqdMaN1+saN4w5yWTFMA6H3GU2yNf5nRHx4flyzCN6Jvl67Hq2cUTsrtzNj4i4QapoGDtd0/HsQLkU+OHk+/cEMilyJvW9trrc+xbHkdeIb9NoJ8peY62u95N9Jp/6kvR48uKwC9MvzNeRJZlnV8b/DDnA+EbPUsSWJF0G3I08qf8O7RrfSjoOuD+5S1uzLVAlrU0OFBeu0QTeMO6ygV46ziR2fR/3JOl8ZtnGPip3jtAsOzaoso9QOTeeSSZCnsqMypmobBY68jxNm+mpw04coyWomloiBrk+uskSMTrveqaG/cF6Uu66tOPwfpB0a+D4iHjofItdZj3XpuOMu3Lr9m3I8++7EfHHBjH/SG6xfhhwSrTrHbDIzkqzPTafqFPfuJH4m5BNZH9BLtmt6gvVcXA/xL8gIh6g6Ttonhdj9lQcidvtvO5B0/v9QI4vqvv9jMS+nKzseDN5U/LOiPhu5TF3WWos6cVkM/2mvflG4q8GrB8RV7SIV2KOLvMfKnR+1SDucr3Glev/2VHRq6jXMavT7n0ldo9dz84me1WdVSoDNwY+ExEPrDzcYXy1G1lFc1uy8j4qqvmGuN8HNge+FxGbS1oX+HREPKoybs973+ZL7nqNtXreT05kRVFEHAccJ+lBEfGdDvH3aBlPZc2gsindIpm5RlnrxzaIsThfon551SJKQqh1WS4AmmoSFuRN8FcqwnWZ6ev9Ph4olyyty/TSyV/Uxo2IH2tqK80jJX0PqE1sSNKDI+Ks8sW21O969jrgQOAuwMykXpCN68dWBkCvp7zflH0b3hSV24m2ThIVXwQeIOnkiNiBrM5pqemSNi2hP5ik6v5gna1LVoENbiyPzbvYw4y7pNcCv43cdvjhwH0lfTIirh4nrnJ3zB+WpCRMVbusL2n92iRtRKwj6V5kWf9blb0aroj6Pl6/Lq/Fp8vXewK/rozZ2xtaB9SiS4JvS1ajnFPOv5rB+IfLTcmRwDEdEr9DNfFvlMuYfs3Uxhk1upzX6rTMISLOA+6j2fv9jJ0kKv4vsifPX8j+RK30Wmr8THJb9WEi4yDgO2R1dBVJO5PLrm4BbKRsYvym2kRt9Ov7uFyvcRHxp/KZUqPLMUfEUcrddjcpD7XcMbLHrmddKnSL44CrybFhy/fDXyPiX5JuUvZr+j25S2mtnve+rduYdBtr9byfnMhE0YhdJV1KnnTfJHeheGlEfHrJv7Zk5ebkIHLLU1G/XOfy8vf5Nce1JFF2H5F0Bxosv5sRu8sWqJK2JJfYbMj0gVFVJljSh8kM87Dt4HMlPSoiXrCEX1us6LNcadRThxLSEdcA55eTf2ySXkR+qPyOqX4xQZ4rNXptY78f8PEyqBW5Fr12oPxFctnEgeTSlE3Ic6RVOeVnySa6w7bce5IViY9sFL+lBZJeDWwyW6XcuNVxI1ovaevdH6ynTwLnzngtPtExdovr9LHAlpLuRpafH0f293jcmPFeTt78ztbfrkWSdk2yZ8UG5OfIWrTpi7UHed0cXt/TymPzVqcbyp2W/iPjiYiHlCqlfYELJJ0LHBkRJzV6ireUz5GXk4mANcm+SrV6nXvNlzkAlJn7twF3iojHStqMrPqsXmpMflbfhdxl5wzg9EZVYr2WGvfszfcG4IFkuwoi4iJle4kqWrR9AEztWvfyyO3BxzF6jQtyDNP1GhcRtctiZ16XmxxzuVE/CvgZ+X5YT9I+EXF6bWzgecBRQ6KWsqtxTcCIOEnShUxV6O7fokK36NXz5/xyTh9O9lf6CzB2lYukNSP7ujap4F+M/ck2Jn8nJx6atO4oWo+1Bt+T9AIW7c039n3URC49GwxlYZJ2JQc0LyM/qGp3UvkxsHNEXL7UH54nJO1CDsbvRGZqNwAuj4h7LfEXly32T5m9Eqp2W8MryG0Yv8/I4D4qt/+W9EPgnlHe3Mo19JdGxD0r465KJjKanYAl7uHkNpdfKA89kSyjvB1wZUS8pCL2j8kZtKrqllnidtvGvsRvstvJjJjPIivY7kLuDLQNWQq9Q2XcRbYO7VnCXUPSpuSNzUvI5TrTtEiKSnoAOcMFjZa0TapSSTMslzi95WtRXudh6WCT2JpqcvtKcgbwA5plOeh8oVzqeGb5c3qLJRmzPMdK5MYL1bvV9KDO/ejKc6w/2+MtKlPL6/sE4BBymYOAV8/nasFO516XnYWUm7IcSe4wu7mklcnlH00+n8qE0VZk/5HnAGtERIvKrSF+s6XGZXJkH6YSDU8APhER72sQ+7sRsY2m7xZVvauxpDcDvyJvIkUu+d+YTKA9LyIePkbMlYBPRkR175l/B8oG7E+NsmSwJLA/ExHVm4doainXxuQSzWsYcynXLBW609RW6JbnOBz4QKOE72jcBWTrh7uSyfb1gb9F6Z02RryvR8ROI/enownfqL0/7a3XWEvSF4Afkq/1m8iJ68sjYv9xY056RdEq5e8dgS9ExDVq08/rdz2SRJJOIrfPvLp8vTa5dvDRDcK/mbzx/XZE3F/S9mQj4Ba2HPn3qsCTaFPC/YeIaN78j9z+dH1gSDitx/QtUcf1KfIEfDQjJ2CDuPcFHlyWcCHpUHJ2bjsyiVbjl+QHU1Mjyby/kn2mmlA2Qz6SvGE4onwgHhARJzYI/2JyQPvdiNhe2fztbQ3inqjcBWgo49+NbFo/FnXcCrYMhA4qA9hvjBtnKS4iK8xWhrzJrL2hlPQ24OAZ186XR8Rraw+2F2XfgEsj4sJyPX6IpJ+OW1o8i+avM/CPUt24N1PVXKss4eeXSEtYOghULx2svQlbHEnHkA2Q/0lWS6wp6f0R8c4ez1cjsoEsEdGrGTLA8UwNxlcFNgKuYMbOjHMh6b5kNdGOZP/Dncu5cidyOdBY7w0tn6X+Pc695sscinUi4vPKTRKIiJsktWrMuh2ZCH8IeRP8dSqayY/EfQFwdERcXZZmrC7p+TFGI+RREfEeSacyleTbt2Hy/lJls/eVlEtgX0xFxcSIXWZMfh9ekoqvKtXBcxa5Sc0Gkm4RETcu/TdWLEnvi4iXaHpj74Wivg/bKjHSVyoi/lfS2J97M4wu5ard/a1rhW6xHfD0koBp2fPnQ2RBwCMi4k2SrgFOJMfkcxYRO5W/u+5EWsaad2d6cUCLSrOmY60Rd4uIJ0l6fOSSymOovCZPeqLoa6V65K/A85RrvP/WIO75kj5HNm4cbQpZO8N1+9GbhIj4s3KpWAv/iFwDvEDSgog4RVL1LAnk2uIZD71PbbZAfb2kjwIzdxqofZ1vDVyuLGUPshz4fElfLfHH/VBpfgIWa5ONJoeEzq2A25YP89pm6lcCp0o6numv8bgNuHtuYw/wjIh4v6RHkxVVTyMTdC0SRX+LiL9JQtIty8zM2I0VRzyLrNAZlrwuAK6X9BzGm9FfHlt0XyjpYzRejqDpSx2Hsv4WSx0fGxELB8Xl2vk4YN4mipheWnwY2WCwRWlxz9d5XzJB8taI+KmkjZjagXAck7p0cLOIuFbSnuS22geQ5+W8SxT1TCyPxJjZQHYLcueWGh8glxO9OkYaY0fE0IdkXKNL/ZuXzHc894ZlDjeSfY9aVYRdr+yjN1RYb0O7yaNTyfPi7cAJDZMOz4qIDw1flOv9s4CxEkUzzpGflT8Lv9fiHAFeBLyGHGd9hpwsenODuDdIejLZXxByImq4z6l5f18JnFXGxaOb1NQuP+9h+Ax6V6f4F5R7kdHeR61ahTRbyhURzyp/b98i3mL06vmzdamg+R4sPKdvMW6wxVVVDRpVVz2TvC6PrkL4Dm0Scq3HWoOht9bVku4N/JZsozO2iU4URcQByv4o15Sb6uuBxzcIvSa5XeLo9q8tBrX/HJ15Ui7faTWQuVq5M8TpwNGSfs/Ixb/GjBNyAVlh1OK9sy+55GoVpvfPqX2daxNYi9P8BCwOJnv9nEoODh8KvE25Y9S3K2P/ovy5RflTq1vPimIoCXwcWRp9qRqVCQK/Uq6R/gpwkqQ/M1V1NrbWM/mxfLbo/gRlOUL5+n/Jvkq1fSv2BzadJblca6WS3Ps7MOwuc8vGz9Hav8rs/X8CHxxKixvF7vI6R8RljGwwELlD3kEV8fZVlpzvFhW7K60Aq5QZ5SeQ/+/+odzieT4aTSzPFGSpf1Ol8mfryhgPW8L3xh4wR8TXyj8vY9EeiEH9dsy9zr1eFWEvJ5PUG0s6C7g9mWxoYR1ymfFDgRdL+he5y+WBlXFXkqSIhe0DVqJu/DLzHBnO5SHJV32ORMQNwGskvb18/ZfamMWewPvJJFkA3wX2Kp+BL6yI+5PyZwGdNmxpJSIuKO+BZ0ef5XLPJbeFHz77zmDMpOQsmm3f3rtCt8SoHhMvxj/K/8PhnL49df0EZ6uqGrSqrtqfPqsQmo+1RhxeqqAOJK/7a1B5TzzpPYpWIRuFDduSngYcFu261Tcl6TFk06rTyA+oh5AXvrGXqYzEvhU5yyDyg2UtsnS3ejCj3HZ3cBM5G/OuqNwCVNIVUbFV5vJWssvHAvchb7TXAA6MiI80iH1HsvIJ4LyIqN5tQB3XoUt67MylS5KeGxGL9L2ZY9wjgTuTVTObkzvsnBoN1orPeJ6HkefIN1vMgvYoT1XH7WtVtonW9H4K1T0yyrXiURFxU+0xzoj7KrI65cjy0L7AVyPi4JbP05Kkc4D3kcm4ncus0SL9rMaM3et1fjDZlHUD8uZ6qGqo7Ud3fkRsufSfnB+U22i/CriYXBq1PrmVb9X23L2UZPp60aBn0GLijza+XwBsAdwu2iyb70L9eiD2OveGsdtGEfFmSesBd4wxe3jMiL0ysCl5Prfc0QlJ9wQeRo5ntwV+saQk4DLGfCd5DRrGVs8BfhkRL6+I2fscuQ+ZhByql/4I7BMRP+jxfDdHyh1lH9Gwcm0YJ18aEfdoFXNG/Gbbt5fxMeQE9bbA/5Svtyf7bfaeyB1bqc7dnfzsOIpMVr82Ir6wxF9ccswFZCX8WW2OcpH4wzj5IrIi6u+SLo153Pu3h0lPFH2UrEYZdpx4GvDPiHhmZdxNgEOBdSPi3sq19LtExFuqDjhjr0OWr0FmKVt1qp845aL3zpJZbRFvtt0hoLKEW7PsDsXIzFSLUl1Jd2bq5mwIXL0OtscHa4l7NnmR/5/y9SuB7SOiqmy1XPjvRzbxvrqUzN85Ii6pPuhOFleeGhG1Ozp9i5zZGi2HfmiLm7NSvfZE4KRSDrwNcFCDAf7HyBuSJksdZ8R+DFM7yZ3UIsHek3I533PJ98JnSmnxkyOietao1+usXMr9UnIGfmEfk9oJB0nvIG+cPsf0ZQ5VSz56flbP8lwrt04OtNQqibyY2K8f+XKYLDo2Ilos9e9Cpcl3h7i9zr1Dmerhcc8y+XBiRIzVw2Mk7iXkzpyfi4if1MSaJfaVZN/GM8lq9nMbTbwsIJNDw0YTJwEfjdLHsSJuz3PkbLJh+Cnl64cDb4uIbSvjHsnsN5S1m6icspi4LSoxupD0SeCeZKVEs+Vyko4DXtQjiahcObKImoS1pBPJJORvytd3JJuyz9vEPUCpyNmBvH86ORr0AlbHzTaUO1vuS7aWeATZBmKViGjRPuB2I18u7P0bEXXVPx12uZz0RNHFMWOHs9keGyPuaeRM1EdGZttbzQQ3TQiMJEeGEtqF36Ldjie3I9fkb1ee40zgTQ1uHi4ndwJo3TStqZFB8qZkGeLQgHtncmBU1TRc0kFkpv1SRpbgRX2Dvp4frOuQjStfATyGXEK4R+0gcWRW9a6RDe/WB/6jxaxqL8q+TUN56v2G8tSIWGKZ8DLEvS153g0Vk6cDb6y9uS6xtyB7hNyLfN/dnlweVJWQm3FDuVA02E1tkvSs5ivxu7zOks6JiKolRYuJ+9NZHm5RqdTls7rHYKs3SUeRy+TOW9HHsiwkbdw6cTEj/g7k1tlNeyB2PPeGXXBGqzxbjGc3IMcXu5Pji88Bn29xU6zsh1mzfGS56nmOdLwfeeLIl6sCuwK/jsqm7Mqd+0bjPhG4KSJeWRO3B0mfioinSboaeO/M7zc4904H7g+cy/RxcvUYvAdJl8fILs5qtLPzJJL0LsrmB9ExoaHGqxAW8xwXROXqCXXY5XKiexSRPX8WDjYk3ZWRWdAKq0fEuZreGqV6JnFxCQHyBnAs0Xenk8FnyWMcPrD2JAcbj1zsbyybJg3eZqPcjePuEXFkSWrcOnIN6JwNH0Llw2SLiLiufP0Gclax1hPInge1jatn02UdekT8UdIuZA+lC8gkQ4uL9Icps6rkznLXkcv9qmZVO+vSJLskhMbe0nIpLiO3CL6BfI2/QvYpGltJjmzSKzkySaLzrjIj16Q1ytet+mGcolz28SWm31xXNYaMfjuTdPmspl8Pr562BvaU9HPyZqfZxIuyn8QrycTy6PLamuqDj0u6C7mr3BnkNvMtt2Tu0gOxY9K7dQ8PYGHlwsHAwcrduA4ke2GsVBsbuFuphGpS0Sfp8xHxZC1m04wG7+Vu5whwpaQDmWpIuxfZMLpKRBw7+rWkz5CTtbVxL5jx0FnKDWDmowcod0P8BTnB1VptT63l7eRScf6Z8vXu1PcznVTPAV4G3CRpaL9SVSSh2TeIGD6b1gBaTdYOWvb+bb7L5aQnil5BDmyHi/GG5OCg1h+VWxsPH9i7kVuh1uqWEBgy7kt7bEx3jIjR3RveImn3cYNJWjMiriVvUJsrM35bkhVAR5JNED9NNl2ssS65G8ngxvJYrSvJwWzz90XrG0oturzvFmQjyN2UvSdrK9ia7oywnHRpkq1cVvNfTG/G2qo0/JPAtUw15nsqOcB90rgBeydHJlC3XWWUzfQ/RemHIemPwN4RcWll6KGaaLSfUJPGkOWYN2N6oqG2sXCvz+puW4p31HPZwdFkomwncjnlPsAfagJGxMPKtX0r4OHA8ZLWiIgl7uI2B1tFhx6I6rdk5xAyeX8HSW+l9PCojAksUlX0TzLp18IRlIo+gIi4RLkb7LhLP4eJkV69VnqeI88A3shUIvKM8lhrd6fBJiozboYXAA8gKybmo8PIysCNmL4bWZNm5BFxWqkiHSYkz42I39fE7CkiXihpV6aqzQ+PiC+vyGNaUToVS8y2QcToyp0WfYRGm3EPy7mf3CBu810uJz1RdBb5AbUDcDW5HeV3GsR9Adl0+h6SriKXRrWYJe+WECBn+hYq5WatGgCfKOkpwLBrzW7kaz2uY8iBwOJOxtqTcFeyjPRCWLjdbouLySeBc5XrViETf59oEPcGctezmSXyVaXF0P6GcjlUsHWZVe0pInYt/3xDuYlYC/hmg9BfIAdIH6VNpeSoe0fEZiNfn6JsulirS3JE0v4R8f6lPTbP9NxV5nDgZTG9H8YRZIPLsUWnbXdL8v7hZKLoBHIL3jOp34Fqts/qqqXARc8txZvqPfFS3C4iPlbOudOA0yRVLd8pVb8PKX9uQy5lPqP+UBc6W9Jm0agH4oj/Gvn3wiU7tUEj4mhJFzDVw+MJ0aaHxznkuPPzwJMiorrKZUTTir4oPVei085LQ1xJd2AkYd0o9p8Z2cWolRmTcwH8jjaJvtHx903ktXO/BnGbi4hDgEMkHRoRz2sdX9KTgXcCp5KvxwckvSIivtj6uVopiaGbZXIIst9Rqd7fYrbv11RBR8RGUt/m973GWmR1VdNdLic9UTTMig/VLlWz4pretPgE4BRykH89ORionQlunhAoM56vBlaTdO3It/5BDqDHpun9j15CvrYiX5O/MH3AtMyidObvuBzhxogIle2MlTvCVYuIt5b1n8PON/tGRIvtrr/KVN+j1rrcUJZYzXf6ouOsak8zljrenty5bayljiNuiohD649uVhdK2iYivgug3Or6/KX8zrLolRzZh9wieNTTZ3ls3ui4RAXgVsM5XZ7r1BbXOUlrMb0v1mlkP7raJMlu5C6G34uIfcvs7aeX8jtLVW56H1n+2xcMy4IbaD7Y6qj3xAvkeALgN5J2BH7N1O5O4zqVPOa3Ayd0qELchhxvNe2B2GvJjqRDgM9GxIdqY82wd1TuULsEXSr6lNuAH0RWzohGPTeVy+XfDdwJ+D3ZL/RyZky0jhm7SwVwRNy6VP+MjrVaLPO/Z8xoRi/plg3idtMjSVS8hqxA/D0snKD8NjBvE0XGy4BnM70yZ1BdBV3uIY8nd7luruNYa2NyIm49Mm+xNZW5nklvZn3ZjFnxWR+bQ7yZTYuPIz+gWjUt3me2xyPiqNken2NVJYcHAAAcgElEQVTst5Pr0Ddh5MOkwY17N5JOjogdlvbYGHH/i/xQfRQ5CH0GcExE9FjbPK+pX4PFLjt9ldjNd0boaXSpY0RsolxL/4WIqFrqqOyB9XsycTaaWB57fbSmej+sQl7nflG+3gD44bjXzlmeZ/WIuKFBnD3ICYDtmF5tsCa5w2XVtaKnjktUKFWNFzK9H8YDRqrbxo17LPADpu8kunnUN2Y/NyIeWComtierXy6Pyi2Jy43NE1n05uxNNXFL7G5bik8aSTuR5996ZI+QNcnG+mNPcJTlug8mB8pbkZWj34mIJv1C1GG3oRJ35pKdLYH31y5zK+PD3cn33JfJpFGL5H03yr6gh5MTT3+mVN83eI1/DOzc+rNf0sXkDeS3I+L+krYH9oqI6kqaEvswFt0xcmZica5xe+2qemFEbLG0x24ONGM3PGVz6Iuj0w55NhnUt/l9r7HWJRFx3zJ5/WbgXcDromKTkkmvKGo6Kx6dmxa3SAgtwZVkw+lpHya06S3xYOCiiLhe0l7AFsD7xi3Jk7QqsDqwTqlKGWZA1yQrMapExLskPYqsNtuUPElOqo3bmvo3boRODRbJgcuw09f2JbnztqX8zrL6Efn/bmUASev3Kv9spNdSxyGx/IqRx2orBHr1fgBA0oPIhr9rAOtL2hx4TkQ8f8yQZ5Mz1OswfeboOqBqh7bloMsSlWLohzE0Oj2DNv35No6I0V123ijpogZxzy+JgcPJG6m/kP9vax1HLgm7gIZLuiXtPeOhLSS16KnUVacqT8gb67+Rr3WTkvmIuFrZX3I9ctyyLZnAbqLX8iWmV279g+wtUZ1oKOPDo0oi6onAQeWz7+61sTu6iuwDeQpZYXYt+blVm6j9XacJon9ExJ8kLVDu2HaKpPc1it2rArjpWEvSf5Dj7NUk3Z/p4+/Vaw92Qn1DizaHPmEFHo/Ngfr0P4SsxtlL0s9o3/y+11hrSFLvCBwREcdLGrdnHDChiaIZs+JnS5o2K97gKbo0LVbuOPF2Fn1DtygNfzH9btwPBTYvN30vJ3umfAp42JjxnkMuZbsT5ea6uBb4YMVxLlQSQ/MuOTRD78aN0O+GsstOX5JeRJZj/o684A3N41pcmHvptdSx+dLMjjdPg/eRDUO/Wp7vYkkPXfKvLF453p9LeiTw14j4VynxvwdTu1DMS72WqBQbkzfYC8jP8R3ISYHa8+SvkraLiDNh4STBXytjAryQrAxbl6z0XB/42xJ/Y9ncJSJ67J45usviquTreyH1PZW6WVzlAQ0mi4AfSPod+flxBnBmbYl8SRL9kOxVdSi5lHsSmuC/itwi+doyCbMF2VaglbuR17dhWdR8dhzZH/RCcjlilbLkDDKx/Dlyg4jRatqqHeuAq5Ube5wOHC3p94z00hvHSIXZ1yS9gEV3jKzdIan1WOvR5LLtuzC9ncZ1ZBuLm6Mg+91uV74+nLx+zkvlc/kN5DViZaYSGC3uJSeK+vU/hDxX1maq3cjp5PWuhV5jraskfYQcZx1Uqq4X1AScyKVniyspHjQoe30N2X18tGnx5yLi7ZVxzyRvgt9LLmfbl+yr8LqauCX2eRGxVclIbh0Rf5d0aUS0WHt9YeROVK8DropsalldoirpRS2Xg2nRHbmmqV3fPokkbUmuv96QqcRwdUa8LH3Zl7wx2YEsO18lIh5XGffH5Pv3TzVxlid1Wuo4S1UD0GympAtJ50TE1pK+FxH3L4+1WOp4AflhvTa5icF5ZIKuxSYDXcyyROUBwCG1S1RK7CvIiqUfMNLsvcFn3/3IUuhh95s/A/tERFX1lnIL7X8Bj4iIe5bKlxMjYqul/OrS4h4OfCDabqs+2/PchlwK1CMp1USZQBsmi+43TBbVlrKPxF+fPAcfDDwOuDoi7lcRb0FEzOuNCmbTo7S/xD2YHGteCXwW+EpEVN+USHoSmdi6TtJrycTWW6Ki2etI7B9ExL1r44zEO7L8c2avLchxS9UuYmUSZ9hCe0/yOnd0zXhD2QNr9HinjUFrb95HxlovIZO+rcZaT4yIY5f+k//+ZrufGc7zFXVMSyLph8BLWXSZ48SMm1spn3tD/8PNVfofRsSjGsTeH3gmmfwVeX0+osV9a8ex1urAY4DvR8SPJN0RuE9EnDhuzImsKOo9Kx79mhavFhEnS1L5b3hDuQGqThTRaYvu4jpl0+y9gIcq1++OXSI+Mmt01ci/Fxp31ijKjlyS3kwuVxmab+8J3HG8o+1PnRo3Fkczyw1lrYjYtVSY/Yncnes2wDsahP4l83R3ocXpuNRx4qoagF9K2hYISauQicQWs+KKiBsk7Qd8OCIOblSm21PPXWX+EBFfaxRr1OVkr7uNyXP6GnJwVLvMb+sy2fA9yB2ClFuj19oOeLoaNyyexfW0aQrdU5cqTwBJdyETRA8hB+WXkrO2Ne5WEojrRsS9Jd0X2CUiqsrkl4Pmpf3Fz8ht5TeMiE9IWl/SJhFRW4V4YER8oSS2Hknu7nQouayi1tmS7tMqURsR+8LC3iD7D4myklierWntXOOPVg81aQUxVP5KWg14PnlNCrLy7rAG8bvsqhoRxyob09+L6Sscqvu7TQpJzyP/n91V0uhn3K3JCan56pqI+MaKPoh5Yqg0v0nSmmRfz/Uaxd4P2Ga4bkg6iKzSbVHg0GWsFdkb9EsjX/+Gyg0GJjJRtDyU2ZbqGZcZ/l6SLD+S9EJyffcaLQL3+jApdieXDewXEb8tM4vvrIi38xK+F4y8yce0y4wKhkOVjQZbJOR6OJgOjRuLLjeUkl5M7jhwLHlztitZkll7Ab0SOFW528Bo+XbtjoNd9VjqGBEvGv16qGpo+RwdPJfciezO5PXtRHIgVkvK/kd7MpVsWalB3G56LB0c8XpJHwVm7qBZe+0cXUpyVWWsUf+QtBJTuyPdnjaJ68c2iLEISaNNmheQZe2f7/FcDfWcLPoFWcX3toh4bqOYR5D91z4CEBGXSDqGTJbMZ81L+4v7UKruyB4/15Gfr1VVd0xPbB3eMLEF/RK19x2tpiqJ5ftXxuw9KXcUOVl0SPn6qeWxJzeIDUBEnNYqlqTDyJ5E25PtJHYDWi2NnhTHAN8gK8EPGHn8ugZLBns6RdI7WXSZY+t71kkw9D88gqn+h99pFFuMVGwx1Q6jhV5jreacKFq+9icvzC8mS5a3Z6phbTMtP0yKl0bEq0bi/0LS2Evahlmjjq6XtCd5Ux3AHlSuQ++sV+NG6HdD+UyySqB1pv0X5c8typ95r/Pgc9T1QM/kQwubzlwOVtZe187O7Q/8N/DliLhUudvOKUv5nRWuVFdtyPQduVpUhO1L9jFZhamES4ske6+eP4eQS7nvIOmt5E3Ja2uDRsTPS2XjUP17RkRcXBsX+A+mmsjfRF6TXtggbjedJ4vuTyYFnirpAHLDgdMi4mMVMVePiHOlaePuVs3ee3oyWdr/rsiG3Hdk+oYD4+pVddcrsQWdErXAAklrR8SfYeEy3hb3Kz0n5e4d03cNPUXSZR2ep5VtyxLKSyLijZLeTSZNbjYi+6xdQ94jTJKhGnDLkceqt4SfRDG1Ucphkr4JrFm7fGvEkcA5ZfknZMVPzWfeqF5jreacKFpOymzq7hHxX2TGs3eypKVHkQ0cRz12lsfmrFPp61PJqob3kxfPs8pj84r6N26EfjeUXTLtUXYenDD/v717j7WsLO84/v0xgwwgA5hqhRBGBYROuag4gQpIEaWiFiIFDV5Qo2iRxhG1JlpAA6hV8IKSSACrCLSJg1YFYVApVwEvM1hRGUK1ghoRpQKj3Omvf7xr9+wznJkDZ6+1117r/D4JOWevM/udZ8I5+6z9vO/zPI3cfEq6iKl+BwuAv2DyTzV8htIDY7Zrj1v12nmI7UMG12z/nJJwn1iSzqMcK/4hUz8rpp7SwWWuodfRDGotJRmwfUFVZn0gVa1/HT8vVQ+Bo5l6PTtf0lk19BBYuO6Gi6RafueNQ92bRS5N6X8G/IySlHsdZZjFKDfNv5e0A1OnzA5nxCPy49DE0f5KU6fumkpsNdkG4uPA9ZJWVI+PAD5Uw7pNbsrVOoV5DAbDBO6TtC3wP0xwi4aYYruWyZN9Y/sXNa/3CUlXMtXkvK42NNDQvVYTkigaE9uPVjXinTFUv7vDDPW7I482buroa/Viceio64zBcAnefcBBQ4/rSOZAc28oa820r5MUeYzhJMEEaurm87Shzx8BbrP9qwb+npFVZWEvAJ4q6V1DX1rMiCViXXztrDwfWGo3MjHiOklLbde9Y91Yzx/ba6hnKumwN1PjycYO96xolKQfAJtQfu9fA7ywhiTBsZTpQrtI+jWlh9frRlyzy5o6dXcf8BVJT6vaBkD9P4e1sv3F6ntucELisFFe68a0KbcnU1OYoUx2vEXVlOYG+qaN6qKqZOdUSvmLKeU70QHzvb/UuNTdhkZTU9sXAm9Smf7ZZH/FkSVRNF43Vr0PVjBUClXTL6kmNF2/28jRV0mLKG8g1n0RHWliRt3GUIIHDb2hbCDTPkiKHEYp+zi/enwk8NsR1m1M0zeftq9SmeAw6FFx6yjrNWxjSr+1hZQ31QP3Ut7wjKprr51QGsg/nWZOSewN/LCBhE5TpSRNqftkY1d7VjTtYNu/q3PB6lTgi1UmUW1ke22d63dNg6fuDqGc0NmW0uh1e0qiaOSJuE2q7lnqum8ZbMqZ5jblOlFGMmQN8KhLU+ullFO/X205pngc0l+q017RdgBPlJrZ7IyZaGr05zBPWgJjXZLOs/362a7NYd3BKO0bKAmCu4Cf2N5xxHVXUH4JvobSFPK1wM22l4+yblNUxuKeAtxP6SmxO6Uv1PkbfOLjW/tmSvlL01OBaiHpB7afP9u1SaDmR/m+irLbd2W1/n7AP9q+cJR1myDpctsHSvqS7dqadw6t35nXzqHTcVsAz6HcwA0nEEc+HSdpyUzXGywFmUjV6bU3UE5iQDnZ+AXbn2ovqv5Y53TgY3iEIQNVr5y/47E9vLIrXiOVQR4vAr5t+7mSDgBeZ7uuCYydofVMU5vE3yNNqzZod69O655M2aw70XYd0/CiQUP/7wYfnwxcanu/WZ/cE1XfsvWa5xs7tcqJojGQ9FGXZtCX2F4x6xMmz7SdJ0kLKcdsR3VxdfT1Y5Ru9VCy46Pa0fYRkg61fa7KJJVrali3KQfZfq+kV1JG5B4GXM3UqZpRdG2Xa3NJz6p2m5H0TGDzlmOakRse5Qv8E6V08M5q3acC3wYmLlEEbKPSuHk3lek00xJnHnEax5hO39XlNMq//6OUxMXA4NrI5ltCaH0a7iHQKWqmqf4Ws/+ROfsapZHsKoYSqVG7h23fJWkjSRvZvkLSfE2kNjJNraOGp+Gd7Xqn4UWz7q8+DvpL3cX86y+1iqlN2u2BP1Sfb0UZPjHpg186I4mi8XiZyqSQ91FKJzpB0vuA9wObSrp3cBl4iNJbYFSnAcdQTkpcT0nmfLaGdR+uPt4taVfgDsrN86Qa/By+HFhh+x5plOqJKR18Q3kccGVVtytgCfC2dkOaVVM3nxsNkkSVu6hvWk3dTgROALYD1j1lMOdpHJLea/tjkj7DDD2sbE9cQ+tBI2FJG8/QEHnTdqLqr7p7CHRY7U313exwgc5Mfem4u6sTB1cDF0i6k8meAtukpqapdVGT0/CiWYNN9uH+UnVssneG7WcCSDqbMg33kurxwUzfoIsRzdcXyHFbScl2Pnko4QLNjdGuhe2PAB+R9BHb72vgrzgXWEtp4gilVOyLlCkdozirOtVxAvB1Su+UE0dcs0kXS1pD2SU4pjo58sAsz+kl2ysl7USZ1Aawxvak7zY3dfO5UtJlwL9Vj18NXFLDurWryuEulHQCcAbwbEp/sFFrmwc9KiZ5esw0aYgcLam9qX7DidrOTH3puEMp9xPHUcrwt6SU5M9HTU1T66LGpuFFs2yfXH36ZUkXA4ts39NmTC3a2/bRgwe2L63aeURN0qNojCR9zXYXpnEBIGkX22skzTjaetRyEkk/tb10tmvzQZVcuMdlwtNmwGLbd7Qd17hV//Z3AUtsH10ljXa2fXHLoa2XpKMoJ++m3XzaPm+ENUU5nbOMqbKaa2z/+/qf1T5JR1PG1m9HGQu/N3Cd7QPnuN55tl8vabnt02sMtTGStgS2Jg2RY4wknU5pnl5bU31Jf2v7IklvYOZE0RdHWPunwI50pH9e9EPVuHlwwvU/6h70EdE0SUcAK22vlXQ8pRH5yfOx7LraTL2GqVYdr6VM5fyb9qLqlySKYr0knWX7rZKuYPpN4uCGbk7lJEPrnw+cYfuG6vFewLG2j5rjeo013mxaVSK3lOlT2uZ8E95VKtPDVgFH2d61ShxdZ/s5LYe2QU3cfEq6yfZuo64zTiqjP5cBN9h+jqRdgA/bPmyWp65vvZ8CL6ZMovprHtv7KImXCJpt+C5pGSUZ/gymTkuOlNRJQ/ZmSVrLzCc6J/oke0Rs2DqNyE+hlKDNy0bk1Sb7B4AXUl7vrgZOyr1hfVJ6Futl+63Vpy+jlFLsS/lBHKmXUPVm0pSR2tdJur16vIQyrWyummy82RhJH6C8CV5KKS06GLiWUoY33+xg+9WSjgSwfZ/qatjUINc7yndgtaRltr9f87pNesD2A5KQtEl1InHnEdY7E7gceBYlgTj8veDqesS813DD9/MpZSk3Af9bx4JJCDXLdifvhyJiVsONyM+az43Iq4TQckmb256vvdcalURRPB7nAvdSXy+hV9QR1LoabrzZpMOBPYAbbb9J0p9Tz8SzLnqoavhrAEk7MH8n4uwFvFbSbZTmo10ozfhV1WTxq8C3JP0BmPMbQtufBj4t6bO2j6kryIi+kbQIeDNlSunwydQ6Rn//zvbXa1gnIiJGk0bklWra7jmUXrTbS9oDeJvtt7cbWX+k9Cxm1ZVeQl2ckAQg6fu2l0laBRxAafB9s+1dZnlq70h6CXA85XTVN4F9gDfavrLNuNrQ9dIMSftTGqeutP1Q2/FE9FnVoHcNZSPnJEqvhpttL69h7QOBIymn+2rpfxQREU9c1ZLhpcBNtm+tGpHvZvubLYc2dpK+S9ls/7rt51bXfmx713Yj64+cKBojSfsAH6SUWC1k6oTApJdPrJa09zq9hCZxCtFg4sskxrYh369OYZxNKa/5I3B9uyG15g3AN4ALgZ8Dy23/vt2Q2tGVhND6rDsaPiIataPtIyQdavtcSf9KKROvw5sokyg3Zqr0zEASRRERY2T7PoZee23/BvhNexG1y/Yv1+lQ8ej6/mw8cUkUjdfnKCNKV9Gtb+Q9meolBLA9cMug19CklMLYvqj6eG7bsTxBiymTsq4EVlImnv1og8/or88B+1GO1O4A3Cjp6q5MvIqIaMnD1ce7q+EIdwBPq2ntZbZH6TUWERFRt19W5WeWtDGwnKlDA1GDlJ6NkaTvdrEr/fpKYAYm7eSDpGcD72H6hBZGndLWFEkHUJIj+1ElR4B5mxyRtIAyOesA4O+B++djGV5ExOMl6S3Al4HdgC9QejacaPvMGtb+PHBqRolHRMSkkPRnwOmU6biitKx4R6ae1SeJojGS9M/AAsqRweE6/9WtBdVDkv6TMi1p2skt26taC2oWSY4Uki4HNqeU3l0DXGv7znajioiYvyTdTNnE+G/KvUsXGutHRESPSdrH9ndmuxZzl0TRGEm6YobLntSTLl0laZXtPduO4/FKcmSKpE9SSh0fBL4DXA1cb/v+VgOLiJhgkj4MfMz23dXjrYF32z6+hrU73Vg/IiL6R9Jq28+b7VrMXRJF0RuSnlJ9+g7gdzz25NZEHkVMcuSxJG0BvJFSQvh025u0G1FExOSSdONg6svQtdwwR0REr0j6K+AFwDuBTw59aTHwStt7tBJYD6WZ9ZhJejnwl8CiwTXbJ7UXUa+sokxiGbS/f/c6X5/I6XK2j4NpyZHPA08H5l1yRNI/UHo17Qn8AvgX6pvcExHRVwskbWL7QQBJmzIPf4dERETvPYnSh28hsMXQ9XuBw1uJqKeSKBojSWcCm1H60JxD+Wb+XqtB9YjtZ8L/3yC/HdiXkji6htKzaCIlOTLNIuATwCrbj7QdTERER1wAXF41noYy0r5rE0AjIiI2yPZVwFWSvpAS6Gal9GyMJP3I9u5DH58MXGp7v7Zj6xNJX6JklS+oLr0G2NL2q9qLav0kvYeSGEpyJCIi5kTSwcCB1cNv2b6szXgiIiLqJulTtt8p6SLKgYBpbB/SQli9lBNF4zXoOXOfpG2Bu4BtWoynr3a1vXTo8RWSJnasr+3T2o4hIiK6zfalwKVtxxEREdGg86qPef/UsCSKxutiSVsBpwKrKVnQc9oNqZdWS9rb9g0AkvYCftByTBEREbWSdK3tfSWtZfrO6mCE/eKWQouIiKid7VXVx6vajqXvUnrWEkmbAIts39N2LH0j6WZgZ+D26tL2wC3AI5Qb593bii0iIiIiIiLmTtI+wAeBJZTDL4MNkokcXtRFSRSNkaQjgJW210o6HngecLLtG1sOrVckLdnQ19P4LCIi+kLSAuAntndpO5aIiIhxkLQGOI4y9frRwXXbd7UWVM+k9Gy8TrC9QtK+wIspJWhnAnu1G1a/JBEUERHzhe1HJd0iaXvbt8/+jIiIiM67p+rNFw1Jomi8BtnOlwNn2f6GpFPaDCgiIiI6b2vgJ5K+B/xpcDHTXyIioqeukHQq8BXgwcFF26vbC6lfUno2RpIuBn4NvIRSdnY/8D3be7QaWERERHSWpP1nup5mnxER0UeSrpjhsm2/aOzB9FQSRWMkaTPgpcBNtm+VtA2wm+1vthxaREREdFjVn28n29+u7jcW2F7bdlwRERHRPUkURURERHSYpKOBtwJPsb2DpJ2AM20f2HJoERERtZN04kzXbZ807lj6aqO2A4iIiIiIkRwL7APcC2D7VuBprUYUERHRnD8N/fcocDDwjDYD6ps0s46IiIjotgdtPyQJAEkLgRwZj4iIXrL98eHHkk4DLmspnF7KiaKIiIiIbrtK0vuBTSW9BFgBXNRyTBEREeOyGbBd20H0SXoURURERHSYpI2ANwMHAaLsqp7j3ORFREQPSbqJqZOzC4CnAifZPqO9qPoliaKIiIiIiIiI6IRq0ufAI8BvbT/SVjx9lERRRERERIdJegVwMrCE0n9SgG0vbjWwiIiI6KQkiiIiIiI6TNJ/AYcBN6XcLCIiIkaVZtYRERER3fZL4MdJEkVEREQdcqIoIiIiosMkLaOUnl0FPDi4bvsTrQUVERERnbWw7QAiIiIiYiQfAv4ILAKe1HIsERER0XFJFEVERER027a2d207iIiIiOiH9CiKiIiI6LZLJB3UdhARERHRD+lRFBEREdFhktYCm1P6Ez0MCLDtxa0GFhEREZ2URFFERERERERERADpURQRERHReZK2BnaiNLQGwPbV7UUUERERXZVEUURERESHSXoLsBzYDvghsDdwPfCiNuOKiIiIbkoz64iIiIhuWw4sA26zfQDwXODudkOKiIiIrkqiKCIiIqLbHrD9AICkTWyvAXZuOaaIiIjoqJSeRURERHTbryRtBXwV+JakPwC3tRxTREREdFSmnkVERET0hKT9gS2BlbYfajueiIiI6J4kiiIiIiIiIiIiAkiPooiIiIiIiIiIqCRRFBERERERERERQBJFERERERERERFRSaIoIiIiIiIiIiKAJIoiIiIiIiIiIqLyfwayDQQf8PMhAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"YO6d6VYi4aJQ"},"source":["## Most occurding `fromloc.city_name` tagged entities"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":386},"id":"rlcEvP9tOSiy","executionInfo":{"status":"ok","timestamp":1649993282639,"user_tz":-300,"elapsed":2826,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"48f7f1db-2b80-467a-ed86-c031d1f268fa"},"source":["ner_type_to_viz = 'fromloc.city_name'\n","ner_df[ner_df.entities_aspect_class == ner_type_to_viz]['entities_aspect'].value_counts().plot.bar(title='Most often occuring fromloc.city_name labeled entities in the dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f6b8aef33d0>"]},"metadata":{},"execution_count":7},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"6R2-0v5Z4hMJ"},"source":["## Most occurding `flight_time` tagged entities"]},{"cell_type":"code","metadata":{"id":"ks6NDXg7RXG3","colab":{"base_uri":"https://localhost:8080/","height":368},"executionInfo":{"status":"ok","timestamp":1649993287152,"user_tz":-300,"elapsed":622,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"778137e3-f6a1-407a-f94d-4493728f9251"},"source":["ner_type_to_viz = 'flight_time'\n","ner_df[ner_df.entities_aspect_class == ner_type_to_viz]['entities_aspect'].value_counts().plot.bar(title='Most often occuring ORG labeled entities in the dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f6b89995550>"]},"metadata":{},"execution_count":8},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"67MNeUed5W0y"},"source":["x"],"execution_count":null,"outputs":[]}]}